Identification and validation of Asteraceae miRNAs by the expressed sequence tag analysis

Gene 493 (2012) 253–259

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Identification and validation of Asteraceae miRNAs by the expressed sequence tag analysis Aboozar Monavar Feshani a, Saeed Mohammadi a, Taylor P. Frazier b, Abbas Abbasi a, Raha Abedini a, Laleh Karimi Farsad a, Farveh Ehya a, Ghasem Hosseini Salekdeh a, c, Mohsen Mardi a,⁎ a b c

Department of Genomics, Agricultural Biotechnology Research Institute of Iran, Karaj, Iran Department of Horticulture, Virginia Tech, Blacksburg, VA 24061, USA Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran, Karaj, Iran

a r t i c l e

i n f o

Article history: Accepted 14 November 2011 Available online 25 November 2011 Received by A.J. van Wijnen Keywords: Asteraceae microRNA qRT-PCR Safflower Sunflower

a b s t r a c t MicroRNAs (miRNAs) are small non-coding RNA molecules that play a vital role in the regulation of gene expression. Despite their identification in hundreds of plant species, few miRNAs have been identified in the Asteraceae, a large family that comprises approximately one tenth of all flowering plants. In this study, we used the expressed sequence tag (EST) analysis to identify potential conserved miRNAs and their putative target genes in the Asteraceae. We applied quantitative Real-Time PCR (qRT-PCR) to confirm the expression of eight potential miRNAs in Carthamus tinctorius and Helianthus annuus. We also performed qRT-PCR analysis to investigate the differential expression pattern of five newly identified miRNAs during five different cotyledon growth stages in safflower. Using these methods, we successfully identified and characterized 151 potentially conserved miRNAs, belonging to 26 miRNA families, in 11 genus of Asteraceae. EST analysis predicted that the newly identified conserved Asteraceae miRNAs target 130 total protein-coding ESTs in sunflower and safflower, as well as 433 additional target genes in other plant species. We experimentally confirmed the existence of seven predicted miRNAs, (miR156, miR159, miR160, miR162, miR166, miR396, and miR398) in safflower and sunflower seedlings. We also observed that five out of eight miRNAs are differentially expressed during cotyledon development. Our results indicate that miRNAs may be involved in the regulation of gene expression during seed germination and the formation of the cotyledons in the Asteraceae. The findings of this study might ultimately help in the understanding of miRNA-mediated gene regulation in important crop species. © 2011 Elsevier B.V. All rights reserved.

1. Introduction MicroRNAs (miRNAs) are short (~21–24 nt), non-coding, singlestranded RNAs that are generated from stem–loop precursors by the DICER-LIKE1 (DCL1) enzyme in plants. They play a critical role in regulating gene expression at the post-transcriptional levels (Bartel, 2004) and have been shown to function in many biological processes including development (Yang et al., 2007), hormone signaling, and response to biotic (Zhang et al., 2006a; Jagadeeswaran et al., 2009) and abiotic (Frazier et al., 2011; Phillips et al., 2007) stresses. In plants, miRNAs have been shown to regulate gene expression by primarily promoting cleavage of target mRNAs (Bartel, 2004). However,

Abbreviations: ABA, Abscisic acid; ARF, Auxin responsive factor; BLAST, Basic local alignment search tool; CT, Cycle threshold; DCL1, Dicer-like 1; EST, Expressed sequence tag; GRF, Growth regulation factor; MFEI, Minimal folding free energy index; miRNA, MicroRNA; NCBI, National Center for Biotechnology Information; nt, Nucleotide; premiRNAs, miRNA precursor; qRT-PCR, Quantitative real-time PCR. ⁎ Corresponding author. Tel.: + 98 261 270 0845; fax: + 98 261 270 4539. E-mail address: [email protected] (M. Mardi). 0378-1119/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gene.2011.11.024

translational inhibitory methods have also been observed (Bartel, 2004; Carrington and Ambros, 2003; Voinnet, 2009). There are different approaches for identifying miRNAs (Zhang et al., 2006a) and their target genes (Jones-Rhoades and Bartel, 2004; Unver et al., 2009). These include experimental methods such as direct cloning and deep sequencing as well as homology-based computational methods such as the expressed sequence tag (EST) and GSS analysis. A large number of miRNAs are evolutionarily conserved in the plant kingdom and have been identified in mosses and ferns to higher flowering plants. This attribute has been used as a practical indicator for the identification and prediction of miRNAs by homology searches in other species (Zhang et al., 2006b). Identifying miRNAs using EST analysis (Frazier and Zhang, 2011) has some advantages over other methods (Zhang et al., 2008). It has been suggested that most of the miRNAs predicted by EST analysis can be recovered by high throughput deep-sequencing (Kwak et al., 2009). The Asteraceae family is the largest plant family on Earth, comprising more than 23,000 genetically diverse and ecologically successful species (Stevens, 2001). Several of these species are an important source of oils for human consumption and other species

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are crucial components in industrial applications (Jeffrey, 2001). A number of major and minor crops, such as Lactuca sativa, Helianthus annuus, carthamus tinctorius, Cynara cardunculus, and Cichorium intybus, as well as numerous ecologically-important taxa including many weedy and invasive species (Funk et al., 2005), are members of the Asteraceae. To date, miRNAs within the Asteraceae have been identified only in lettuce (Lactuca sativa) (Han et al., 2010), safflower (Carthamus tinctorius) (Li et al., 2010), zinna (Zinnia elegans) (Zhang et al., 2005), and sunflower (Helianthus annuus) (Zhang et al., 2005) and even so, only one study has identified Asteraceae miRNAs using EST analysis (Han et al., 2010). Even though these studies have been conducted on various members of the Asteraceae, few plant species in this family have been fully sequenced and newly identified plant miRNAs are continuously being deposited in miRbase (GriffithsJones et al., 2008). These factors have made it difficult to perform a comprehensive analysis of conserved miRNAs in the Asteraceae. Therefore, we used EST analysis to thoroughly identify potentially conserved miRNAs and their putative target genes in the Asteraceae. In this study, we performed a BLASTn search, using known plant miRNAs, against all EST sequences of Asteraceae in NCBI GenBank. We also predicted the target genes for the newly identified potential miRNAs by a similar homology-based method. Next, seven predicted miRNAs were validated by quantitative real time PCR (qRT-PCR) in two species of this family, safflower and sunflower. Using qRT-PCR, we also investigated the expression pattern of six miRNAs in safflower cotyledons over 5 consecutive days, from emergence of radicals until the appearance of the first leaves.

2.4. Identification of miRNA target genes Target genes for the putative miRNAs were identified using psRNATarget server, an automated plant miRNA target prediction server (Dai et al., 2010). The predicted mature miRNAs and protein coding sequences of all plants were used as inputs. The psRNATarget parameter settings were set as default. In the case of sunflower and safflower, we also used their ESTs in addition to the mentioned protein database.

2.5. Plant material Safflower and sunflower seeds were surface sterilized in 4% hypochlorite for 10 min and then allowed to germinate on water-soaked filter paper. After 48 h, uniform seedlings were transferred into 1 × Hoagland's nutrient solution and grown hydroponically, with aeration, for 3 days. Samples were harvested from complete 5-day-old

2. Materials and methods 2.1. Sources of microRNAs and EST sequences To search for potential conserved miRNAs in the Asteraceae, 1955 unique plant miRNAs, along with their precursor sequences, were selected and downloaded from the 3034 plant miRNAs deposited in the miRNA database, miRbase (Griffiths-Jones et al., 2008). EST and mRNA databases were obtained from the NCBI database (www.ncbi. nlm.nih.gov). 2.2. Blast search against Asteraceae ESTs The non-redundant miRNA sequences were used as BLAST search queries against the 999,614 ESTs of Asteraceae (omitting the ESTs deposited for lettuce) as previously described (Zhang et al., 2005). The BLAST search was carried out using Blast-2.2.24+. Blastn parameter settings were as follows: expect 1000; the number of descriptions and alignments were 500. The word-size between the query and database sequences was set at 7. The EST sequences which closely matched (no more than 4 mismatches) the previously known mature miRNAs were selected and used to search against the plant protein database using Blastx in order to remove the protein-coding sequences. To select non-coding ESTs, we considered the E value less than e-5 along with the identity percent less than 25. 2.3. Secondary structure predictions The secondary structures of potential miRNA precursor sequences were predicted and generated using Quikfold provided in the DINAMelt Server (Markham and Zuker, 2005). To investigate the primary and ancillary criteria of miRNAs annotation (Meyers et al., 2008), the outputs were then further analyzed as previously described (Adai et al., 2005). Predicted miRNAs and their related information were recorded and finally, redundant ESTs (with high similarity, E value less than e-100) were discarded to obtain the mature sequences of new miRNAs and their potential precursor sequences in each species.

Fig. 1. Properties of predicted miRNAs. (a) Distribution of the length of predicted premiRNAs. (b) Distribution of the MFEI of predicted pre-miRNAs (c) Distribution of the length of mature-miRNAs in Asteraceae.

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Fig. 2. Two pre-miRNA that include two mature miRNA in each of their arms. (a) EST 68258862 from Taraxacum (b) EST 113304967 from Helianthus.

seedlings of safflower and sunflower. We further collected safflower cotyledons samples in 5 consecutive days at 24 h intervals. The samples were immediately frozen in liquid nitrogen and kept in −80 °C for RNA extraction. 2.6. RNA extraction and Real-Time PCR analysis The samples of three independent biological replicates were ground into fine powders and total RNA enriched for small RNAs was isolated from the cotyledons of each sample using Trizol reagent (Invitrogen) according to manufacturer's instructions with some modification as previously described (Xue et al., 2008). The quality and quantity of isolated RNAs were measured using a Nanodrop ND-1000 (Nanodrop technologies, Wilmington, DE, USA). Stem– loop RT and gene-specific real time PCR primers for eight miRNA were designed according to Chen et al. (2005) (Supplementary Table S1). miRNA stem–loop reverse transcription was performed using Superscript III First-Strand Synthesis System (Invitrogen) according to Varkonyi-Gasic et al. (2007). qRT-PCR was performed (three biological cDNA replicates each by three technical replicates) using iQ SYBR Green Supermix (Bio-Rad) on a Bio-Rad System (MyiQ™ Single-Color) according to the manufacturer's instructions. qRT-PCR data were analyzed based on the method described by Pfaffi (Pfaffl, 2001). 18s rRNA was used as a reference gene and the miRNA sample at time = 0 was selected as the calibrator. Statistical significance was determined using one-way analysis of variance followed by LSD post-test. All statistical analyses were performed using the SPSS 16.0 (SPSS Inc, Chicago, USA). 3. Results 3.1. Properties of Asteraceae miRNAs Blastn search of known conserved miRNAs against the 999,614 EST sequences of the Asteraceae resulted in identification of 151 conserved miRNAs belonging to 11 genus of Asteraceae family including Artemisia (4 miRNAs), Helianthus (48 miRNAs), Barnadesia (7 miRNAs), Carthamus (7 miRNAs), Centaurea (18 miRNAs), Cichorium (26 miRNAs), Cynara (7 miRNAs), Guizotia (3 miRNAs), Parthenium (11 miRNAs), Zinna (2 miRNAs) and Taraxacum (19 miRNAs). All 151 predicted miRNAs were classified into 26 miRNA families (Supplementary Table S2). Sixty-six of the potential Asteraceae miRNAs (43.4%) were found on the 5′ arm of the premiRNA stem–loop hairpin whereas 86 miRNAs (56.6%) were found on the 3′ arm (Supplementary Table

S2). The average length of the potential Asteraceae pre-miRNA precursor sequence was 100.46 ± 36 nt with the majority (48.02%) of the pre-miRNA precursors in the range of 80–100 nucleotides (Fig. 1a). Also, most Asteraceae mature miRNAs (47.36%) were found to be 21 nucleotides in length (Fig. 1b). The GC content of the pre-miRNA precursors ranged from 31% to 58%, with an average of 42.3 ± 4.5%. The potential Asteraceae pre-miRNA precursors had a mean minimal free folding energy index (MFEI) of 0.93 ± 0.17, indicating that these newly identified miRNAs are more likely to be actual miRNAs than any other kind of non-coding RNA (Zhang et al., 2006c) (Fig. 1c). 3.2. Three phenomena in Asteraceae miRNAs We found two putative Asteraceae pre-miRNA precursor sequences that contain two mature miRNAs in the 3′ and 5′ arms. One of these sequences, located within EST 68258862, included miR479 (MIMAT0014338) in its 5′ arm and miR171f (MIMAT0001756) in its 3′ arm (Fig. 2a). This phenomenon was also observed in another pre-miRNA sequence, located within EST 113304967, and included miR171c (MIMAT0018334) and miR171b (MIMAT0017518) as 5′ arm and 3′ arm of the pre-miRNA, respectively (Fig. 2b). It has been reported that multiple miRNAs can accumulate from the same precursor by different mechanisms (Meyers et al., 2008). Antisense miRNAs (Supplementary Table S3) were also identified in our study. Moreover, we identified four potential miRNA clusters, involving two different miRNA families, in four genus of Asteraceae. Zinna contained one cluster (EST 24250052) that included miR473b and miR477f. Helianthus contained one cluster (EST 113176785) that included one miR473b and two miR477b. A cluster found in Parthenium (EST 294693975) was similar to the Helianthus cluster that contained three miRNAs (Fig. 3). Another miRNA cluster found in Barnadesia (EST 211668570) that included two miR477b. 3.3. Experimental validation of predicted miRNAs Stem–loop RT-PCR and qRT-PCR are reliable methods for detecting and measuring the expression levels of miRNAs. In this study, we designed stem–loop primers based on the newly identified potential miRNAs in sunflower and previously identified conserved miRNA of safflower (Li et al., 2010). We adopted this technique to validate and measure the expression levels of eight potential conserved miRNAs (miR156, miR159, miR160, miR162, miR166, miR396, miR398 and miR477) in both safflower and sunflower seedlings. We successfully validated the existence of all miRNAs except for miR477 (Fig. 4).

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3.4. Temporal expression patterns of validated conserved miRNAs

4. Discussion

To examine whether these validated seven miRNAs are growthstage-specific or not, we studied their expression in 5 consecutive days at 24 h intervals in cotyledon of safflower by qRT-PCR (Fig. 5). We were not able to identify miR396 and 477 in safflower cotyledon in any of the investigated stages. For miR156, miR160 and miR398 the lowest expression levels were found in Time 0, while miR159 and miR166 showed the lowest expression levels in 96 h and 72 h, respectively. The miR162 did not show significant changes (p > 0.05) in different stages.

In this study, we performed an EST-based miRNA discovery in the Asteraceae followed by validation of a few miRNAa using qRT-PCR analysis. The number of predicted miRNAs (151) was equal to 0.0152% of Asteraceae ESTs that was similar to previous reports in switchgrass (as 0.0277%) (Xie et al., 2010b), soybean (0.0175%) (Zhang et al., 2008), purple false brome (0.05%) (Unver and Budak, 2009) and some other plant species (0.010%) (Zhang et al., 2006b). Also the length distribution of miRNAs and their precursor sequences were similar to previous reports in other plant species (Sunkar et al., 2005; Unver and Budak, 2009; Xie et al., 2010b; Yin et al., 2008; Zhang et al., 2008). Moreover the GC content of the pre-miRNAs is within the valid range (Zhang et al., 2008). Uracil has been shown to be the first nucleotide in mature miRNAs (Unver and Budak, 2009; Yin et al., 2008; Zhang et al., 2008) and in agreement with these previous results the majority of miRNA sequences we detected (96 of the 151 mature miRNAs) have Uracil at the first position of their 5′ end. MFEI, a reliable criterion to distinguish miRNAs from all coding and non-coding RNAs, was significantly higher than that of tRNAs (0.64), rRNAs (0.59), or mRNAs (0.65) (Zhang et al., 2006c) and

3.5. Identification of miRNA targets Using the newly identified miRNAs as queries in the psRNATarget, we could predict the potential mRNA targets both in the mRNA database of all plant species and in the sunflower and safflower EST databases. We found 115 and 15 miRNA target ESTs in sunflower and safflower, respectively, and 433 target mRNAs in all other plant species (Supplementary Table S4). No targets were found for miR538, miR473, miR172, mi845 and miR3630.

Fig. 3. miRNA clusters found in Asteraceae. Secondary structure of pre-miRNA with three mature miRNA and its sequnce.

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similar to what previously reported (Yin et al., 2008; Zhang et al., 2008) the identified pre-miRNAs had a high MFEI with an average of 0.97 ± 0.12.

Fig. 4. Confirmation of nine predicted miRNAs in safflower and sunflower seedling. CT analysis of qRT-PCR data showing expression levels of seven miRNA in safflower and sunflower seedling.

Antisense miRNAs of miR156, miR157, miR162, miR164, miR169, miR172, miR396 and miR827 families have recently been identified in plants (Frazier et al., 2010; Xie et al., 2010a,b; Zhang et al., 2008). In this study, we found 27 pairs of sense and antisense miRNAs in different genus of Asteraceae that belonged to different miRNA families (Supplementary Tables S3, Fig. 6). Seven miRNAs among them belonged to new families which have not been reported previously (miR171, miR393, miR403, miR408, miR479, miR477, miR1433 and miR398). Since sequence of mature miRNAs and also their precursors are different from their antisense, it was proposed that they might have different targets or implement their functions through different mechanisms in plants (Tyler et al., 2008).

Fig. 5. CT analysis of qRT-PCR data showing expression levels of six miRNA in safflower cotyledon in 5 consecutive days. Time 0 that was considered as the control time was equals to emergence of radicals. * showing p b 0.03 compared to Time 48 h; ** showing p b 0.03 compared to Time 0; *** showing p b 0.006 compared to Time 0 and **** showing p b 0.03 compared to Time 24 h.

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Fig. 6. An example of sense/anti-sense pairs found in Asteraceae. EST 125410284, from Helianthus has two miR398b as a sense/anti-sense pair.

Some miRNA genes can be found in the genome as clusters, implying that they are transcribed as a single transcriptional unit and can give rise to multiple mature miRNAs (Merchan et al., 2009). miRNA clusters were reported for a few miRNA families (miR156, miR169, miR395, miR172, miR399 and miR1219) in different plants (Jones-Rhoades and Bartel, 2004; Zhang et al., 2006b, 2008; Xie et al., 2010b; Frazier et al., 2010) and they have been shown to be conserved in different plant species (Sunkar and Jagadeeswaran, 2008). A miRNA gene cluster in sunflower (containing one miR473b and two miR477b genes) has a ~ 170 nt spacer between each two miRNAs and interestingly we found this cluster in Parthenium which is another genus of Asteraceae, with the same length of spacer. miR473 and miR477 may have evolved by duplication of target sequences and has been shown to regulate same mRNA targets (Fattash et al., 2007) and both of them may be involved in the formation of specialized woody tissue in trees (Lu et al., 2008). miR477 has also been shown to play roles in plant response to cold stress (Lu et al., 2008). Moreover, we found two pre-miRNAs that have two putative mature miRNA sequences in each of their arms. These kinds of miRNAs were reported as multifunctional miRNAs (Meyers et al., 2008). Further study is necessary to identify whether both arms of premiRNAs creates mature miRNA or not. qRT-PCR was used to validate the existence of eight conserved miRNAs that were identified by EST analysis in two species of Asteraceae, sunflower and safflower. As shown in Fig. 4, the expression of seven predicted miRNAs was validated in 5-day-old seedling of these two species. Possible roles and targets of these miRNA in different developmental stages, physiological situations and in response to environmental cues have been listed in Supplementary Table S5. We were not able to identify miR477 in any of the seedlings by qRTPCR, which could be due to its low level of expression in plants (Mica et al., 2009) or lack of expression in our target tissue (Kwak et al., 2009). We then compared the expression pattern of six miRNA in five stages in cotyledon of safflower (Fig. 5). miR156 was found to be highly expressed in seed, leaf and petal of safflower (Li et al., 2010) and it showed significant upregulation during development of cotyledon. Liu et al. (2007) showed that negative regulation of ARF10 by miR160 plays important roles in seed germination and cotyledon growth. The differences in the expression pattern of miR160 suggest similar functions for development of cotyledon. Our results showed miR166 and miR398 have the highest and lowest level of expression among all investigated miRNAs in cotyledone, respectively, that is consistence with previous studies (Li et al., 2010). On the other hand, differences in expression level of miR389 were significant during different stages of cotyledon growth

and it showed that miR389 plays important roles in the development of safflower cotyledon. It has been shown that miR396, that is barely detected in plants (Frazier et al., 2010) targets seven Growth Regulating Factor (GRF) genes (Jones-Rhoades and Bartel, 2004) that encode putative transcription factors associated with cotyledon growth (Kim et al., 2003) but we could not detect it in safflower cotyledon. We used psRNTarget algorithms and identified putative miRNA targets among the EST entries of sunflower and safflower and also in the mRNA database of all other plants. Previously, a wide range of targets for different families of miRNAs were predicted and validated (Kantar et al., 2010; Xie et al., 2010a,b; Yang et al., 2007; Yin et al., 2008). miRNA targets predicted in our study (Supplementary Table S4) included transcription factors, DNA replication proteins and also genes involved in metabolism, response to stresses, signal transduction and development. Even though miRNAs generally function as negative regulators of gene expression by mediating the cleavage of target mRNAs (Llave et al., 2002) or by repressing their translation (Chen, 2004), the cleavage of target mRNAs appears to be the predominant mode of gene regulation by plant miRNAs (Sunkar et al., 2005). Results of psRNTarget showed that 4% of miRNA targets that are found in mRNA database of all plants and 25% of miRNA targets that are found in the ESTs of sunflower and safflower are predicted to be repressed by translation inhibition mechanism. Further investigation is necessary to validate identified miRNA targets.

5. Conclusion The role of plant miRNAs, as well as their target genes, in plant growth and development will eventually contribute to the creation of a novel transgenic technology for the improvement of plant yield and it will help feed the exponentially increasing population of human beings (Lewis et al., 2009). Prediction and characterization of microRNAs will provide an extended infrastructure for studying of microRNAs. We showed that some conserved microRNAs might be involved in the development of cotyledon in safflower. Supplementary materials related to this article can be found online at doi:10.1016/j.gene.2011.11.024.

Acknowledgements This work was supported by the Agricultural Biotechnology Research Institute of Iran (ABRII).

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