High-throughput sequencing of small RNAs and analysis of differentially expressed microRNAs associated with pistil development in Japanese apricot
© Gao et al.; licensee BioMed Central Ltd. 2012
Received: 21 November 2011
Accepted: 25 July 2012
Published: 3 August 2012
MicroRNAs (miRNAs) are a class of endogenous, small, non-coding RNAs that regulate gene expression by mediating gene silencing at transcriptional and post-transcriptional levels in high plants. However, the diversity of miRNAs and their roles in floral development in Japanese apricot (Prunus mume Sieb. et Zucc) remains largely unexplored. Imperfect flowers with pistil abortion seriously decrease production yields. To understand the role of miRNAs in pistil development, pistil development-related miRNAs were identified by Solexa sequencing in Japanese apricot.
Solexa sequencing was used to identify and quantitatively profile small RNAs from perfect and imperfect flower buds of Japanese apricot. A total of 22,561,972 and 24,952,690 reads were sequenced from two small RNA libraries constructed from perfect and imperfect flower buds, respectively. Sixty-one known miRNAs, belonging to 24 families, were identified. Comparative profiling revealed that seven known miRNAs exhibited significant differential expression between perfect and imperfect flower buds. A total of 61 potentially novel miRNAs/new members of known miRNA families were also identified by the presence of mature miRNAs and corresponding miRNA*s in the sRNA libraries. Comparative analysis showed that six potentially novel miRNAs were differentially expressed between perfect and imperfect flower buds. Target predictions of the 13 differentially expressed miRNAs resulted in 212 target genes. Gene ontology (GO) annotation revealed that high-ranking miRNA target genes are those implicated in the developmental process, the regulation of transcription and response to stress.
This study represents the first comparative identification of miRNAomes between perfect and imperfect Japanese apricot flowers. Seven known miRNAs and six potentially novel miRNAs associated with pistil development were identified, using high-throughput sequencing of small RNAs. The findings, both computationally and experimentally, provide valuable information for further functional characterisation of miRNAs associated with pistil development in plants.
KeywordsJapanese apricot microRNA Pistil abortion qRT-PCR Solexa sequencing
Japanese apricot (Prunus mume Sieb. et Zucc) is an important economic fruit crop in China and Japan, with more than 200 cultivars in China . Japanese apricot fruit has consistently been one of the most valuable processing materials used in the food and wine-making industries and is believed to contain many physiochemicals beneficial to human health. However, the phenomenon of imperfect flowers is common and seriously affects production yields. The percentage of imperfect flowers depends on the cultivar; the highest is 75.15% and the average is 35% . Imperfect flowers are characterised by pistils below the stamens, withered pistils or the absence of pistils, and hence, they fail to bear fruit . Several environmental factors and physiological processes have been shown to affect pistil development [1, 4]. Previous research indicated that several miRNAs and multiple target genes are involved in flower development in model plants [5–12]. More recently, real-time quantitative reverse transcription polymerase chain reaction and in situ hybridisation showed that the PmAG mRNA was highly-expressed in the sepals, carpels and stamens, and a weak signal was detected in the seeds and nutlets. No expressions were detected in the leaves or petals, but no significant differential was expressed between perfect and imperfect flowers . Meanwhile, comparative proteomic analyses were performed on perfect and imperfect flowers, and several differently-expressed proteins were identified . However, the type of molecular mechanism involved in pistil abortion remains unknown in Japanese apricot.
Small RNAs (sRNAs) are low molecular weight RNAs with regulatory functions. Based on differences in biogenesis and action, sRNAs are grouped into two categories: short interfering RNAs (siRNAs) and microRNAs (miRNAs) [14, 15]. MicroRNAs are non-coding RNAs, 21–24 nucleotides (nt) long, which regulate gene expression at the post-transcriptional level [16–18]. In plants, miRNAs are processed from the stem-loop regions of long primary transcripts by a Dicer-like enzyme and are loaded into silencing complexes, where they generally direct the cleavage of complementary mRNAs . Identified in plants less than 10 years ago [19, 20], miRNAs are already known to play numerous crucial roles at each major stage of development, typically at the cores of gene regulatory networks, targeting genes that are themselves regulators, such as those that encode transcription factors, suggesting that plant miRNAs are master regulators [18, 21]. Among non-transcription factor targets, many miRNAs encode F-box proteins or ubiquitin-conjugating enzymes implicated in targeting selected proteins for proteasomal degradation, indicating miRNAs play a role in regulating protein stability and plant development [22, 23]. miR156, miR163, miR169, miR172, miR398 and miR399 play important roles in flowering-time regulation and belong to ambient temperature-responsive miRNAs in plants [9, 24]. miR172 has also acquired specialised species-specific functions in other aspects of plant development, such as cleistogamy and tuberisation .
The fact that a large number of the known miRNAs in the plant kingdom, from mosses and ferns to higher flowering plants, are evolutionarily conserved has been used as a practical indicator for the identification or prediction of miRNAs using homology searches in other species [25, 26]. Recently developed, next-generation, high-throughput sequencing technologies provide a powerful tool for identifying, as well as quantifying, miRNAs. These technologies open up the possibility of exploring sRNA populations in economically important species such as Arabidopsis thaliana[27, 28], Oryza sativa[29, 30], Populus trichocarpa[31, 32], Vitis vinifera, Arachis hypogaea, Citrus sinensis, Citrus trifoliate, Medicago truncatula[37, 38], Glycine max, Carthamus tinctorius Cucumis sativus, Rehmannia glutinosa and others. By means of high-throughput sequencing, miR164 and miR169 were shown to be drought-responsive miRNAs in Medicago truncatula. miR167, miR1857 and miR172a are involved in the mutant trait formation of lycopene accumulation in sweet orange .
Although miRNAs have been extensively studied in the past, there has be no systematic examination of miRNAs performed on the Japanese apricot. To investigate the role of miRNAs on the pistil development of Japanese apricot, high-throughput sequencing technology (Solexa) was employed to survey sRNA populations from perfect and imperfect flower buds.
High-throughput sequencing of small RNAs from Japanese apricot flower bud tissue
Summary of data cleaning produced by small RNA sequencing (perfect and imperfect flower buds)
3′ adapter null
5′ adapter contaminants
Smaller than 18nt
Distribution of small RNAs among different categories (perfect and imperfect flower buds)
Small RNA sequences and mean frequencies in both perfect and imperfect libraries
Perfect and imperfect common
Identification of known miRNAs and evolutionary conservation
Known miRNAs in perfect and imperfect libraries
Identification of potentially novel miRNAs/new members of known miRNA families and nucleotide bias
Novel miRNAs/new members of known miRNA families in perfect and imperfect libraries
Length of mature(nt)
Counts of miRNAs/miRNA*s
Length of precursors(nt)
Differentially expressed miRNAs between perfect and imperfect flower bud libraries
Small RNAs in the perfect and imperfect libraries were enriched for lengths of 21–24 nt (Figure 1), a typical range for plant miRNAs. For small RNAs shorter than 23 nt, the imperfect flower buds had higher expression levels than the perfect flower buds, while for small RNAs longer than 23 nt, the opposite was the case. In addition to the different length distributions of small RNAs, the proportions of each type of small RNA in the two libraries were also different (Table 2). The proportion of miRNAs in perfect flower buds was higher than that in imperfect flower buds. However, rRNAs, snRNAs and tRNAs in imperfect flower buds were higher than in perfect flower buds. In summary, the small RNA transcriptomes of the two libraries exhibited certain differences with respect to length distribution and composition.
miRNAs expressed differentially in perfect and imperfect libraries
1.45e − 06
6.41e − 12
9.05e − 51
6.82e − 25
1.97e − 37
4.14e − 99
Prediction of potential targets of differentially expressed miRNAs
The high frequency of ‘regulation of transcription’ and ‘response to stress’ terms are easily explainable, since miRNAs are involved in diverse regulatory events . Moreover, many flower developmental genes were also predicted targets of miRNAs. One gene involved in ovule development was further analysed (miR319, miR319a and miR319e and putative target auxin response factor 2 (ARF2) [Ppa022314m]). The results implied the possible roles of miRNAs in the regulation of biological processes involved in pistil abortion.
High-throughput sequencing of Japanese apricot small RNAs
Most conserved miRNAs in plants have been identified by traditional Sanger sequencing or computational approaches [43–47]. However, a large number of non-conserved or species-specific miRNAs in plants usually accumulate at a lower level than conserved miRNAs, and are typically not easily revealed using traditional sequencing methods . Therefore, the construction and high-throughput sequencing of small RNA libraries seems to be the most efficient method for miRNA identification [36, 49]. The aim of this work was to identify the evolutionary known and potentially novel Japanese apricot-specific miRNAs recovered from perfect and imperfect flower bud libraries, and to analyse the differentially expressed miRNAs associated with pistil development. In our study, small RNA libraries of Japanese apricot from the Solexa platform, one of the newly-developed high-throughput sequencing technologies, generated shorter reads (up to 35 bp) but yielded 1–3 million reads per sample. This method has been most popularly employed to discover miRNAs in various organisms. As expected, a large number of reads were generated by our small RNA libraries, of which 61 are known to belong to 24 families. In addition, 61 potentially novel miRNAs/new members of known miRNA families were identified in Japanese apricot.
Taking a broader view of the high-throughput sequencing of small RNAs in Japanese apricot, it was observed that small RNAs of 24 nt dominated the library of unique species, as has been reported for many other plant species, such as Arabidopsis thaliana, Citrus trifoliata, Medicago truncatula[37, 38], Citrus sativus and Citrus Sinensis. However, the most abundant small RNAs in the imperfect library were 21 nt long. Normally, the length of small RNAs is between 18 nt and 30 nt. Length distribution analysis is a helpful way to assess the composition of small RNA samples. For example, miRNA is normally 21 nt or 22 nt long, whereas siRNA is 24 nt long . The data obtained here imply that the most abundant small RNAs are miRNAs and siRNAs in perfect and imperfect flower buds, respectively.
The overall distribution pattern of small RNAs (21 nt sRNAs = 30.33%, and 24 nt sRNAs = 35.63%) in Japanese apricot is significantly different from that in Pinus contorta, a conifer species in which 21 nt RNAs are more abundant (>50%) and 24 nt RNAs are less frequent (2.5%). A striking difference also exists when comparing Japanese apricot small RNAs with monocot species of rice. When compared with eudicotyledon species of sweet orange, the difference is not as noticeable, but still exists, such that 24 nt sRNAs are most frequent (>50%) while 21 nt sRNAs are less common (<20%). These analyses indicate that the small RNA transcriptome is complicated across plant species and can be significantly different between phylogenetically distant plant families.
miRNAs identified in plant flowers
Arabidopsis miRNAs  regulate multiple developmental events. In the past, miRNA identification in flowers using sequencing approaches has been common, and has been applied in the study of Arabidopsis, tomato , orchid , Boechera, maize , cotton , safflower and grape [56, 57]. In this study, high-throughput sequencing of extracts from Japanese apricot flower buds led to the identification of 61 known miRNAs, belonging to 24 families, and 61 potentially novel miRNAs/new members of known miRNA families. Analysis of these revealed that some miRNA families are expressed in the flowers of other plant species, but they are not flower-specific. miR156/miR157, miR166 and miR167 were represented most frequently in the libraries in this study. The involvement of three miRNA families (miR172, miR159/miR319 and miR156) in flowering-time regulation has been recently demonstrated in other investigations . miR164, miR319, miR159 and miR167 specify particular cell types during the later stages of flower development . Furthermore, the comparison of these species’ miRNAs showed that miR156/miR157 and miR172 may be components of a regulatory pathway mediating the transition between the vegetative and reproductive phases in plants. In addition, miR172 regulates stem cell fate and defines the inner boundary of APETALA3 and pistillata expression domains in Arabidopsis floral meristems . By targeting APETALA2 and type III homeodomain-leucine zipper (HD-Zip) genes, miR166 regulates the temporal program of floral stem cells . It is believed that miR167, like miR160, targets mRNAs coding for ARF, which are DNA-binding proteins that are thought to control transcription in response to the phytohormone auxin [27, 60]. Transcriptional regulation is important for many of the diverse developmental responses to auxin signals, which include cell elongation, division and differentiation in both roots and shoots .
miRNAs possibly involved in the regulation of pistil abortion in imperfect Japanese apricot flower buds
The characterisation and comparative profiling of entire sets of small RNAs (small RNA transcriptome), especially miRNAs, provides the foundation for unraveling the complex miRNA-mediated regulatory networks controlling pistil abortion in imperfect Japanese apricot flower buds. In this study, a number of miRNAs were shown to be differentially expressed between perfect and imperfect flower buds. Compared with the perfect library, four known miRNA genes and three potentially novel miRNA/new members of known miRNA families genes were expressed exclusively in imperfect flower buds. On the other hand, it was found that two potentially novel miRNAs/new members of known miRNA families were perfect-specific. Moreover, a total of seven known miRNAs and six potentially novel miRNAs exhibited significant expression changes between the perfect and imperfect libraries.
The relationship between the sRNA genes and the miRNA target genes is one of the hot spots in the phenomenon of ‘miRNA-associated transitivity’. Target prediction of these differential miRNAs could provide information on the biological processes regulated by miRNA. The annotations of these potential miRNA target genes provided an alternate view of gene regulation of the pistil abortion trait formation in imperfect flower buds. It was discovered that these groups of predicted miRNA-target genes are possibly involved in the pistil abortion trait formation.
The majority of genes encoding transcription factors or F-box proteins have a significant role in the plant development [44, 62]. In the present study, it was found that the predicted targets of miR319, miR319a, miR319e, miR160, miR393, miR394, miR6274, miR6295 and miR171d were either transcription factors or F-box proteins. In addition, it is predicted that miR319/miR319a/miR319e target ARF2 genes and that miR160a targets ARF16/17. Auxin regulates a variety of physiological and developmental processes in plants. ARF has been reported to regulate flower and leaf development [8, 63, 64]. ARF2 is a transcriptional suppressor that has been found to be involved in ethylene, auxin, ABA and brassinosteroid pathways, in order to control the onset of leaf senescence, floral organ abscission and ovule development . ARF2 promotes transitions between multiple stages of Arabidopsis development and positively regulates flower development . In this study, the expression of miR319/miR319a/miR319e was shown to be higher in imperfect than in perfect flower buds. Consequently, the expression of ARF2 was repressed by these miRNAs and thus regulated pistil development. Moreover, TCP2 (TEOSINTE BRANCHED/CYCLOIDEA/PCF) transcription factor genes and MYB33, which belong to a GAMYB-like family of transcription factors, are also targets of miR319/miR319a/miR319e in our prediction, which agrees with previous reports [10, 66]. Therefore, it is conceivable that the over-expression of miR319/miR319a/miR319e may contribute to an increase in imperfect flower ratios in pistil development.
The present study first comparatively constructed the miRNAomes between perfect and imperfect Japanese apricot flowers and identified 61 known miRNAs belonging to 24 families. Comparative profiling revealed that seven known miRNAs exhibited significant expression differences between perfect and imperfect flower buds. In addition, 61 potentially novel miRNAs/new members of known miRNA families were also identified, by the presence of mature miRNAs and corresponding miRNA*s in the sRNA libraries. Comparative analysis showed that six potentially novel miRNAs were differentially expressed between perfect and imperfect flower buds. Target predictions of the 13 differential miRNAs resulted in 212 target genes. GO annotation revealed that highly-ranked miRNA target genes were those implicated in the developmental process, regulation of transcription and response to stress. These findings, both computational and experimental, provide valuable information for further functional characterisation of miRNAs associated with pistil development in plants.
The frequency of imperfect flowers in Japanese apricot cultivar ‘Daqiandi’ is about 76%. While the pistils of perfect flowers continued to develop, the pistils of imperfect flowers stopped developing in early December and ultimately disintegrated. The present study used these two types of flower buds from this period, from ‘Daqiandi’ trees grown in the ‘National Field Genebank for Japanese apricot’ (Nanjing, Jiangsu Province, China). All the samples were collected and immediately frozen in liquid nitrogen and stored at −80°C.
Small RNA library construction and high-throughput sequencing
In order to construct small RNA libraries, small RNAs were extracted from perfect and imperfect flower buds using a method based on polyethylene glycol (PEG) precipitation combining CTAB buffer . Two small RNA samples were sequenced by the Beijing Genomics Institute (BGI) (Shenzhen, Guangdong Province, China) using the high-throughput pyrosequencing technology developed by Solexa.
Bioinformatics analysis of sequencing data
The raw sequences were processed as described by Sunkar et al.. Following removal of the vector sequences, modified sequences from 18 nt to 30 nt were used for further analyses. To begin with, rRNA, tRNA, snRNA, snoRNA and material containing the poly-A tail were removed from the sRNA sequences. The remaining sequences were compared with rice and Arabidopsis ncRNAs deposited in the NCBI GenBank and Rfam10.0 databases. Then, the unique sRNA sequences were used in a BLASTN search of the miRNA database. Only perfectly matched sequences were considered to be conserved miRNAs.
Potentially novel sequences were identified through alignment with the peach genome sequence on the GDR (http://www.rosaceae.org/species/prunus/peach). Candidate pre-miRNAs were identified by folding the flanking genome sequence of distinct miRNAs using MIREAP , followed by a prediction of secondary structure using mFold v3.5 . The criteria chosen for stem-loop hairpins were those described by Meyers et al. and Wang et al.[42, 55].
Differential expression analyses of miRNAs related to pistil development
The frequency of miRNAs in the two libraries was normalised to one million by the total number of miRNAs in each sample (normalised expression = actual miRNA count/total count of clean reads*1,000,000). Following normalisation, if the miRNA gene expression of two samples was zero, then it was revised to 0.01; if the miRNA gene expression of two samples was less than 1, owing to their too low expression and did not participate in analysis of differential expression.
Prediction of potential target mRNAs for Japanese apricot miRNAs
The rules used for target prediction were based on those suggested by Allen et al. and Schwab et al.. Putative Japanese apricot miRNAs were first blasted against the peach unigene database on the GDR (http://www.rosaceae.org/species/prunus/peach). BLASTN hits possessing less than four mismatches were chosen as candidate targets. BLASTX was then used to obtain their putative functions.
Validation of miRNAs by poly(A)-tailed qRT-PCR
The poly(A)-tailed qRT-PCR was carried out as previously reported, with a minor modifications [67, 73]. This could not only detect the existence of Japanese apricot miRNAs, but also their expression trends in various organs and tissues. Small RNAs were extracted from Japanese apricot flower buds as described above and used in a 50-μl reaction system for adding poly(A) tails with poly(A) polymerase (NEB, USA). Reverse transcription was performed using poly(A)-tailed small RNAs from Japanese apricot flower buds, such that 1 μg of small RNA was reverse transcribed to cDNA using MLV reverse transcriptase (Promega, Madison, USA) and looped antisense primers [5′-CCAGTAGCGTATGATGAGCACAGAGTCTGAGATCACTCGTAGCGAGG-d(T)33-V(A/C/G)N(A/C/G/T)-3′]. According to the manufacturer’s instructions, the mix was incubated at 37°C for 40 minutes. qPCR was performed using SYBR® Green Realtime PCR Master Mix (Toyobo, Osaka, Japan). A list of all the primers used is provided in Additional file 4. For each reaction, 1 μL of diluted cDNA (equivalent to 100 pg of total RNA) was mixed with 10 μL of 2X SYBR Green Reaction Mix (SYBR® Green qRT-PCR Master Mix, Takara). A final volume of 20 μL was achieved by the addition of 5 pmol of the forward and the reverse primers. The conditions for the PCR amplification were as follows: polymerase activation at 95°C for 3 minutes, followed by 40 cycles of 95°C for 20 seconds, 60°C for 20 seconds and 72°C for 45 seconds. The fluorescence signal was measured once every 1°C. Negative PCR controls (no cDNA template) were prepared, in order to detect possible contamination. The specificity of the primer amplicons was tested by the analysis of a melting curve. The CT values were converted into relative copy numbers using a standard curve . The 5 S rRNA was used as a reference gene in the qPCR detection of miRNAs in Arabidopsis[56, 74]. The data was analysed with an R2 >0.998 using the LinRegPCR program .
We gratefully acknowledge support of this research by the Special Fund for Agro-scientific Research in the Public Interest of the Ministry of Agriculture of China (201003058), grants from the National Science Foundation of China (31101526) and the Natural Science Foundation of Jiangsu Province (BK2011642), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
- Chu MY: China fruit records-Mei (in Chinese). 1999, China Forestry, BeijingGoogle Scholar
- Gao Z, Wang S, Zhang Z: Comparative study on flower and fruit characteristics of 29 varieties in Japanese apricot (Prunus mume Sieb. et Zucc.). Jiangsu Agri Sci. 2006, 6: 231-233.Google Scholar
- Hou J, Gao Z, Wang S, Zhang Z, Chen S, Ando T, Zhang J, Wang X: Isolation and characterization of an AGAMOUS homologue PmAG from the Japanese Apricot (Prunus mume Sieb. et Zucc.). Plant Mol Biol Rep. 2011, 29: 473-480. 10.1007/s11105-010-0248-3.View ArticleGoogle Scholar
- Wang S, Gao Z, Zhang Z, Xu J, Hou J: Comparison of the contents of nitrogen, phosphorus and potassium between perfect and imperfect flowers in Japanese apricot (Prunus mume). J Fruit Sci. 2009, 26: 564-567.Google Scholar
- Montgomery TA, Howell MD, Cuperus JT, Li D, Hansen JE, Alexander AL, Chapman EJ, Fahlgren N, Allen E, Carrington JC: Specificity of ARGONAUTE7-miR390 interaction and dual functionality in TAS3 trans-acting siRNA formation. Cell. 2008, 133: 128-141. 10.1016/j.cell.2008.02.033.View ArticlePubMedGoogle Scholar
- Zhu QH, Helliwell CA: Regulation of flowering time and floral patterning by miR172. J Exp Bot. 2011, 62: 487-10.1093/jxb/erq295.View ArticlePubMedGoogle Scholar
- Nag A, Jack T: Sculpting the flower; the role of microRNAs in flower development. Curr Top Dev Biol. 2010, 91: 349-378.View ArticlePubMedGoogle Scholar
- Liu X, Huang J, Wang Y, Khanna K, Xie Z, Owen HA, Zhao D: The role of floral organs in carpels, an Arabidopsis loss-of-function mutation in MicroRNA160a, in organogenesis and the mechanism regulating its expression. Plant J. 2010, 62: 416-428. 10.1111/j.1365-313X.2010.04164.x.View ArticlePubMedGoogle Scholar
- Lee H, Yoo SJ, Lee JH, Kim W, Yoo SK, Fitzgerald H, Carrington JC, Ahn JH: Genetic framework for flowering-time regulation by ambient temperature-responsive miRNAs in Arabidopsis. Nucleic Acids Res. 2010, 38: 3081-3093. 10.1093/nar/gkp1240.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Y, Cao G, Qu LJ, Gu H: Involvement of an R2R3-MYB transcription factor gene AtMYB118 in embryogenesis in Arabidopsis. Plant Cell Rep. 2009, 28: 337-346. 10.1007/s00299-008-0644-4.View ArticlePubMedGoogle Scholar
- Wang J, Czech B, Weigel D: miR156-regulated SPL transcription factors define an endogenous flowering pathway in Arabidopsis thaliana. Cell. 2009, 138: 738-749. 10.1016/j.cell.2009.06.014.View ArticlePubMedGoogle Scholar
- Amiteye S, Corral JM, Vogel H, Sharbel TF: Analysis of conserved microRNAs in floral tissues of sexual and apomictic Boechera species. BMC Genomics. 2011, 12: 500-10.1186/1471-2164-12-500.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang S: Preliminary studies on differences of related characteristics between perfect flower and imperfect flower and protemics in Japanese apricot. 2008, Nanjing Agricultural University, NanjingGoogle Scholar
- Carthew RW, Sontheimer EJ: Origins and mechanisms of miRNAs and siRNAs. Cell. 2009, 136: 642-655. 10.1016/j.cell.2009.01.035.PubMed CentralView ArticlePubMedGoogle Scholar
- Mallory AC, Vaucheret H: Functions of microRNAs and related small RNAs in plants. Nat Genet. 2006, 38: S31-S37. 10.1038/ng1791.View ArticlePubMedGoogle Scholar
- Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR: Real-time quantification of microRNAs by stem–loop RT–PCR. Nucleic Acids Res. 2005, 33: e179-10.1093/nar/gni178.PubMed CentralView ArticlePubMedGoogle Scholar
- Jones-Rhoades MW, Bartel DP, Barte B: MicroRNAs and their regulatory roles in plants. Annu Rev Plant Biol. 2006, 57: 19-53. 10.1146/annurev.arplant.57.032905.105218.View ArticlePubMedGoogle Scholar
- Bushati N, Cohen SM: MicroRNA functions. Annu Rev Cell Dev Biol. 2007, 23: 175-205. 10.1146/annurev.cellbio.23.090506.123406.View ArticlePubMedGoogle Scholar
- Mette MF, Winden J, Matzke M, Matzke AJM: Short RNAs can identify new candidate transposable element families in Arabidopsis. Plant Physiol. 2002, 130: 6-9. 10.1104/pp.007047.PubMed CentralView ArticlePubMedGoogle Scholar
- Llave C, Xie Z, Kasschau KD, Carrington JC: Cleavage of scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science. 2002, 297: 2053-10.1126/science.1076311.View ArticlePubMedGoogle Scholar
- Zhang B, Pan X, Cobb GP, Anderson TA: Plant microRNA: a small regulatory molecule with big impact. Dev Biol. 2006, 289: 3-16. 10.1016/j.ydbio.2005.10.036.View ArticlePubMedGoogle Scholar
- Jones-Rhoades MW, Bartel DP, Bartel B: MicroRNAs and their regulatory roles in plants. Annu Rev Plant Biol. 2006, 57: 19-53. 10.1146/annurev.arplant.57.032905.105218.View ArticlePubMedGoogle Scholar
- Rubio-Somoza I, Weigel D: MicroRNA networks and developmental plasticity in plants. Trends Plant Sci. 2011, 16: 258-264. 10.1016/j.tplants.2011.03.001.View ArticlePubMedGoogle Scholar
- An FM, Hsiao SR, Chan MT: Sequencing-based approaches reveal low ambient temperature-responsive and tissue-specific microRNAs in Phalaenopsis orchid. PLoS One. 2011, 6: e18937-10.1371/journal.pone.0018937.PubMed CentralView ArticlePubMedGoogle Scholar
- Sunkar R, Jagadeeswaran G: In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biol. 2008, 8: 37-10.1186/1471-2229-8-37.PubMed CentralView ArticlePubMedGoogle Scholar
- Song C, Fang J, Li X, Liu H, Chao CT: Identification and characterization of 27 conserved microRNAs in citrus. Planta. 2009, 230: 671-685. 10.1007/s00425-009-0971-x.PubMed CentralView ArticlePubMedGoogle Scholar
- Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev. 2006, 20: 3407-3425. 10.1101/gad.1476406.PubMed CentralView ArticlePubMedGoogle Scholar
- Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL: High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One. 2007, 2: e219-10.1371/journal.pone.0000219.PubMed CentralView ArticlePubMedGoogle Scholar
- Nobuta K, Venu R, Lu C, Beló A, Vemaraju K, Kulkarni K, Wang W, Pillay M, Green PJ, Wang G: An expression atlas of rice mRNAs and small RNAs. Nat Biotechnol. 2007, 25: 473-477. 10.1038/nbt1291.View ArticlePubMedGoogle Scholar
- Zhou M, Gu L, Li P, Song X, Wei L, Chen Z, Cao X: Degradome sequencing reveals endogenous small RNA targets in rice (Oryza sativa L. ssp. indica). Front Biol. 2010, 5: 67-90. 10.1007/s11515-010-0007-8.View ArticleGoogle Scholar
- Barakat A, Wall P, DiLoreto S: Conservation and divergence of microRNAs in Populus. BMC Genomics. 2007, 8: 481-10.1186/1471-2164-8-481.PubMed CentralView ArticlePubMedGoogle Scholar
- Klevebring D, Street N, Fahlgren N, Kasschau K, Carrington J, Lundeberg J, Jansson S: Genome-wide profiling of Populus small RNAs. BMC Genomics. 2009, 10: 620-10.1186/1471-2164-10-620.PubMed CentralView ArticlePubMedGoogle Scholar
- Lu YD, Gan QH, Chi XY, Qin S: Identification and characterization of microRNAs and their targets in grapevine (Vitis vinifera). Agr Sci China. 2008, 7: 929-943. 10.1016/S1671-2927(08)60132-7.View ArticleGoogle Scholar
- Zhao CZ, Xia H, Frazier TP, Yao YY, Bi YP, Li AQ, Li MJ, Li CS, Zhang BH, Wang XJ: Deep sequencing identifies novel and conserved microRNAs in peanuts (Arachis hypogaea L.). BMC Plant Biol. 2010, 10: 3-10.1186/1471-2229-10-3.PubMed CentralView ArticlePubMedGoogle Scholar
- Xu Q, Liu Y, Zhu A, Wu X, Ye J, Yu K, Guo W, Deng X: Discovery and comparative profiling of microRNAs in a sweet orange red-flesh mutant and its wild type. BMC Genomics. 2010, 11: 246-10.1186/1471-2164-11-246.PubMed CentralView ArticlePubMedGoogle Scholar
- Song C, Wang C, Zhang C, Korir N, Yu H, Ma Z, Fang J: Deep sequencing discovery of novel and conserved microRNAs in trifoliate orange (Citrus trifoliata). BMC Genomics. 2010, 11: 431-10.1186/1471-2164-11-431.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang T, Chen L, Zhao M, Tian Q, Zhang W-H: Identification of drought-responsive microRNAs in Medicago truncatula by genome-wide high-throughput sequencing. BMC Genomics. 2011, 12: 367-10.1186/1471-2164-12-367.PubMed CentralView ArticlePubMedGoogle Scholar
- Szittya G, Moxon S, Santos DM, Jing R, Fevereiro MPS, Moulton V, Dalmay T: High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genomics. 2008, 9: 593-10.1186/1471-2164-9-593.PubMed CentralView ArticlePubMedGoogle Scholar
- Song Q, Liu Y, Hu X, Zhang W, Ma B, Chen S, Zhang J: Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing. BMC Plant Biol. 2011, 11: 5-10.1186/1471-2229-11-5.PubMed CentralView ArticlePubMedGoogle Scholar
- Martínez G, Forment J, Llave C, Pallás V, Gómez G: High-throughput sequencing, characterization and detection of new and conserved cucumber miRNAs. PLoS One. 2011, 6: e19523-10.1371/journal.pone.0019523.PubMed CentralView ArticlePubMedGoogle Scholar
- Yang Y, Chen X, Chen J, Xu H, Li J, Zhang Z: Differential miRNA expression in Rehmannia glutinosa plants subjected to continuous cropping. BMC Plant Biol. 2011, 11: 53-10.1186/1471-2229-11-53.PubMed CentralView ArticlePubMedGoogle Scholar
- Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao X, Carrington JC, Chen X, Green PJ, et al: Criteria for annotation of plant microRNAs. Plant Cell. 2008, 20: 3186-3190. 10.1105/tpc.108.064311.PubMed CentralView ArticlePubMedGoogle Scholar
- Sunkar R, Zhu JK: Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell. 2004, 16: 2001-10.1105/tpc.104.022830.PubMed CentralView ArticlePubMedGoogle Scholar
- Jones-Rhoades MW, Bartel DP: Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell. 2004, 14: 787-799. 10.1016/j.molcel.2004.05.027.View ArticlePubMedGoogle Scholar
- Bonnet E, Wuyts J, Rouzé P, Van de Peer Y: Detection of 91 potential conserved plant microRNAs in Arabidopsis thaliana and Oryza sativa identifies important target genes. Proc Natl Acad Sci USA. 2004, 101: 11511-11516. 10.1073/pnas.0404025101.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang B, Pan X, Wang Q, Cobb GP, Anderson TA: Identification and characterization of new plant microRNAs using EST analysis. Cell Res. 2005, 15: 336-360. 10.1038/sj.cr.7290302.View ArticlePubMedGoogle Scholar
- Yu H, Song C, Jia Q, Wang C, Li F, Nicholas KK, Zhang X, Fang J: Computational identification of microRNAs in apple expressed sequence tags and validation of their precise sequences by miR-RACE. Physiol Plant. 2011, 141: 56-70. 10.1111/j.1399-3054.2010.01411.x.View ArticlePubMedGoogle Scholar
- Moxon S, Jing R, Szittya G, Schwach F, Rusholme Pilcher RL, Moulton V, Dalmay T: Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening. Genome Res. 2008, 18: 1602-10.1101/gr.080127.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Pang M, Woodward AW, Agarwal V, Guan X, Ha M, Ramachandran V, Chen X, Triplett BA, Stelly DM, Chen ZJ: Genome-wide analysis reveals rapid and dynamic changes in miRNA and siRNA sequence and expression during ovule and fiber development in allotetraploid cotton (Gossypium hirsutum L.). Genome Biol. 2009, 10: R122-10.1186/gb-2009-10-11-r122.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang S, Li R, Zhang Z, Li L, Gu X, Fan W, Lucas WJ, Wang X, Xie B, Ni P: The genome of the cucumber, Cucumis sativus L. Nat Genet. 2009, 41: 1275-1281. 10.1038/ng.475.View ArticlePubMedGoogle Scholar
- Xu M, Liu Q, Nisbet AJ, Cai X, Yan C, Lin R, Yuan Z, Song H, He X, Zhu X: Identification and characterization of microRNAs in Clonorchis sinensis of human health significance. BMC Genomics. 2010, 11: 521-10.1186/1471-2164-11-521.PubMed CentralView ArticlePubMedGoogle Scholar
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116: 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Yang X, Zhang H, Li L: Global analysis of gene-level microRNA expression in Arabidopsis using deep sequencing data. Genomics. 2011, 98: 40-46.View ArticlePubMedGoogle Scholar
- Wang Y, Itaya A, Zhong X, Wu Y, Zhang J, van der Knaap E, Olmstead R, Qi Y, Ding B: Function and evolution of a microRNA that regulates a CaZ + −ATPase and triggers the formation of phased small interfering RNAs in tomato reproductive growth. Plant Cell. 2011, 23: 3185-3203. 10.1105/tpc.111.088013.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang L, Liu H, Li D, Chen H: Identification and characterization of maize microRNAs involved in the very early stage of seed germination. BMC Genomics. 2011, 12: 154-10.1186/1471-2164-12-154.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang C, Wang X, Kibet NK, Song C, Zhang C, Li X, Han J, Fang J: Deep sequencing of grapevine flower and berry short RNA library for discovery of novel microRNAs and validation of precise sequences of grapevine microRNAs deposited in miRBase. Physiol Plant. 2011, 143: 64-81. 10.1111/j.1399-3054.2011.01481.x.View ArticlePubMedGoogle Scholar
- Pantaleo V, Szittya G, Moxon S, Miozzi L, Moulton V, Dalmay T, Burgyan J: Identification of grapevine microRNAs and their targets using high-throughput sequencing and degradome analysis. Plant J. 2010, 62: 960-976.PubMedGoogle Scholar
- Zhao L, Kim YJ, Dinh TT, Chen X: miR172 regulates stem cell fate and defines the inner boundary of APETALA3 and PISTILLATA expression domain in Arabidopsis floral meristems. Plant J. 2007, 51: 840-849. 10.1111/j.1365-313X.2007.03181.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Ji L, Liu X, Yan J, Wang W, Yumul RE, Kim YJ, Dinh TT, Liu J, Cui X, Zheng B, et al: ARGONAUTE10 and ARGONAUTE1 regulate the termination of floral stem cells through two microRNAs in Arabidopsis. PLoS Genet. 2011, 7: e1001358-10.1371/journal.pgen.1001358.PubMed CentralView ArticlePubMedGoogle Scholar
- Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP: MicroRNAs in plants. Genes Dev. 2002, 16: 1616-1626. 10.1101/gad.1004402.PubMed CentralView ArticlePubMedGoogle Scholar
- Licausi F, Weits DA, Pant BD, Scheible WR, Geigenberger P, Van Dongen JT: Hypoxia responsive gene expression is mediated by various subsets of transcription factors and miRNAs that are determined by the actual oxygen availability. New Phytol. 2011, 190: 442-456. 10.1111/j.1469-8137.2010.03451.x.View ArticlePubMedGoogle Scholar
- Earley K, Smith M, Weber R, Gregory B, Poethig R: An endogenous F-box protein regulates ARGONAUTE1 in Arabidopsis thaliana. Silence. 2010, 1: 1-10. 10.1186/1758-907X-1-1.View ArticleGoogle Scholar
- Wang L, Hua D, He J, Duan Y, Chen Z, Hong X, Gong Z: Auxin Response Factor2 (ARF2) and its regulated homeodomain gene HB33 mediate abscisic acid response in Arabidopsis. PLoS Genet. 2011, 7: e1002172-10.1371/journal.pgen.1002172.PubMed CentralView ArticlePubMedGoogle Scholar
- Lim PO, Lee IC, Kim J, Kim HJ, Ryu JS, Woo HR, Nam HG: Auxin response factor 2 (ARF2) plays a major role in regulating auxin-mediated leaf longevity. J Exp Bot. 2010, 61: 1419-1430. 10.1093/jxb/erq010.PubMed CentralView ArticlePubMedGoogle Scholar
- Ellis CM, Nagpal P, Young JC, Hagen G, Guilfoyle TJ, Reed JW: AUXIN RESPONSE FACTOR1 and AUXIN RESPONSE FACTOR2 regulate senescence and floral organ abscission in Arabidopsis thaliana. Development. 2005, 132: 4563-4574. 10.1242/dev.02012.View ArticlePubMedGoogle Scholar
- Palatnik JF, Wollmann H, Schommer C, Schwab R, Boisbouvier J, Rodriguez R, Warthmann N, Allen E, Dezulian T, Huson D: Sequence and expression differences underlie functional specialization of Arabidopsis microRNAs miR159 and miR319. Dev Cell. 2007, 13: 115-125. 10.1016/j.devcel.2007.04.012.View ArticlePubMedGoogle Scholar
- Wang X, Xiong A, Yao Q, Zhang Z, Qiao Y: Direct isolation of high-quality low molecular weight RNA of pear peel from the extraction mixture containing nucleic acid. Mol Biotechnol. 2009, 44: 61-65.View ArticlePubMedGoogle Scholar
- Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31: 3406-3415. 10.1093/nar/gkg595.PubMed CentralView ArticlePubMedGoogle Scholar
- Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z: MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res. 2007, 35: W339-W344. 10.1093/nar/gkm368.PubMed CentralView ArticlePubMedGoogle Scholar
- Li G, Li Y, Li X, Ning X, Li M, Yang G: MicroRNA identity and abundance in developing swine adipose tissue as determined by Solexa sequencing. J Cell Biochem. 2011, 112: 1318-1328. 10.1002/jcb.23045.View ArticlePubMedGoogle Scholar
- Allen E, Xie Z, Gustafson AM, Carrington JC: microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell. 2005, 121: 207-221. 10.1016/j.cell.2005.04.004.View ArticlePubMedGoogle Scholar
- Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, Weigel D: Specific effects of microRNAs on the plant transcriptome. Dev Cell. 2005, 8: 517-527. 10.1016/j.devcel.2005.01.018.View ArticlePubMedGoogle Scholar
- Fujii H, Chiou TJ, Lin SI, Aung K, Zhu JK: A miRNA involved in phosphate-starvation response in Arabidopsis. Curr Biol. 2005, 15: 2038-2043. 10.1016/j.cub.2005.10.016.View ArticlePubMedGoogle Scholar
- Shi R, Chiang VL: Facile means for quantifying microRNA expression by real-time PCR. Biotechniques. 2005, 39: 519-525. 10.2144/000112010.View ArticlePubMedGoogle Scholar
- Ramakers C, Ruijter JM, Deprez RHL, Moorman AFM: Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett. 2003, 339: 62-66. 10.1016/S0304-3940(02)01423-4.View ArticlePubMedGoogle Scholar