Characterization of the stress associated microRNAs in Glycine maxby deep sequencing
- Haiyan Li†1, 2,
- Yuanyuan Dong†1,
- Hailong Yin2,
- Nan Wang1,
- Jing Yang1,
- Xiuming Liu1, 2,
- Yanfang Wang1, 2,
- Jinyu Wu1, 3Email author and
- Xiaokun Li1Email author
© Li et al; licensee BioMed Central Ltd. 2011
Received: 21 May 2011
Accepted: 23 November 2011
Published: 23 November 2011
Plants involved in highly complex and well-coordinated systems have evolved a considerable degree of developmental plasticity, thus minimizing the damage caused by stress. MicroRNAs (miRNAs) have recently emerged as key regulators in gene regulation, developmental processes and stress tolerance in plants.
In this study, soybean miRNAs associated with stress responses (drought, salinity, and alkalinity) have been identified and analyzed in combination with deep sequencing technology and in-depth bioinformatics analysis. One hundred and thirty three conserved miRNAs representing 95 miRNA families were expressed in soybeans under three treatments. In addition, 71, 50, and 45 miRNAs are either uniquely or differently expressed under drought, salinity, and alkalinity, respectively, suggesting that many miRNAs are inducible and are differentially expressed in response to certain stress.
Our study has important implications for further identification of gene regulation under abiotic stresses and significantly contributes a complete profile of miRNAs in Glycine max.
Terrestrial plants face serious abiotic stresses (e.g. drought, salinity, alkalinity, cold, pathogen responses and diseases), these are the predominant cause of decreased crop yields . Being one of the major oil crops worldwide, Glycine max faces these challenges posed by environmental stressors. To cope with environmental stresses, crops have evolved sophisticated adaptive response mechanisms . Therefore, unraveling the complex resistant mechanisms of soybeans will provide fundamental insights into the biological processes involved in environmental stimuli, which may prove helpful in alleviating crop losses.
There is increasing evidence that microRNAs (miRNAs), ~21 nucleotides (nt) in length, act as key factors in gene regulation, developmental processes and stress tolerance in plants [3–5]. MiRNAs function by either cleaving their targets (mRNAs predominantly via RISC) or repressing protein translation [6, 7]. Indeed, it has been suggested that a number of miRNAs that participate in stress responses have adapted to environmental challenges. For example, Phillips et al.  reported that miR395, miR397b, and miR402 are involved in stress response. Expression levels of miR393 changed under salinity and alkaline stresses, however, over-expression of miR393 is harmful to plants . In response to environmental stresses, fluctuations in the expression of miRNAs can be induced by many uncontrolled factors, such as drought, salinity, and alkalinity at transcriptional and post-transcriptional levels. It was reported that sulfate starvation lead to the up-regulation of miRNA395  miR398 and miR408 were responded to water deficiency . Furthermore, these inducible miRNAs display different specificity under different stresses. However, our knowledge of the roles played by miRNAs under stress conditions in plants is still limited, especially at the whole-genome level.
In recent years, it has been possible to identify miRNAs through either bioinformatics or sequencing. For instance, various methods have been used to identify miRNAs in rice, wheat, and maize [11–13]. Many bioinformatics approaches and technologies have been developed for rapid and accurate miRNA detection and analysis. Recently, deep sequencing technology is showing significant promise for small RNA discovery and genome wide transcriptome analysis at single-base pair resolution . In comparison with microarray, deep sequencing has several advantages, the major one being its application in comprehensively identifying and profiling small RNA populations that were previously unknown. Deep sequencing has identified many small RNAs in different plants, mutants, and tissues at various developmental stages [15–18]. In this study, soybean miRNAs associated with stress response were identified and analyzed by high-throughput sequencing. One hundred and thirty three known miRNAs corresponding to 95 miRNA families were detected in soybeans under three stress treatments. In addition, 71, 50, and 45 miRNAs were differentially expressed under drought, salinity, and alkalinity, respectively, suggesting that many miRNAs are inducible and are differentially expressed during different environmental stresses.
General features of small RNA transcriptomes under diverse treatments
It is essential to generate a reference set of annotations for exploring the small RNA categories. All identical Solexa reads in each library were sorted into unique sequence tags for further analysis. When aligned, all sequences were read against the Glycine max genome using SOAP2 , about 70% of reads matched perfectly and 30% were from un-annotated genome sites with one mismatch. For instance, in the mock, 7,045,434 (75.4%) clean reads that grouped into 1,609,063 unique reads were matched to the 1 115 Mb genome of Glycine max. Subsequently, for each library approximately 60% of clean small RNAs were identified as products processed from rRNAs, tRNAs, snRNAs, or other non-coding RNAs (Figure 1B). Another fraction (approximately 40%) was predominantly derived from un-annotated or repeated sequences. Large portions of annotated small RNAs were mainly non-coding RNAs. For the mock group, 1 289 824 clean sequences which were classified into 1,474 unique tags were considered to be potential miRNAs. The other two induced by drought and salinity were 1,393,901 (1,512 unique tags) and 1,302,431 (1,503 unique tags), respectively. Notably, in the alkalinity-induced group, 513,021 screened reads (1,062 unique tags) were miRNA candidates, accounting for nearly half of miRNAs of the former three groups. It is estimated that known miRNAs might be the most abundant class of small RNAs regulated at post-transcriptional levels in plant defense.
Known miRNAs in soybean
Many miRNAs of the soybean have been reported in previous studies. Kulcheski et al.  detected 256 miRNAs from drought-sensitive and tolerant seedlings and rust-susceptible and resistant soybeans, of which 24 families of miRNAs had not been reported before. Song et al.  identified 26 new miRNAs in developing soybean seeds by deep sequencing. Joshi  identified 129 miRNAs based on sequencing and bioinformatic analyses, among which, 42 miRNAs matched known miRNAs in soybean or other species, while 87 were novel miRNAs. In another study Chen et al. , reported 15 conserved miRNA candidates belonging to eight different families and nine novel miRNA candidates comprising eight families in wild soybean seedlings. To identify known miRNAs from the soybean in four diverse treatments, small RNA sequences were compared with miRBase 16.0. After a sequence similarity search, 133 known miRNAs corresponding to 95 miRNA families were identified in the soybean (Additional file 3). In addition, four conserved star miRNAs (miR156d*, miR157b*, miR162*, and miR3630*) have also been sequenced. Among them, miR156d*, miR157b*, and miR3630* star sequence expressions were rather low. However, the abundance of miR162* ranged from 125 to 220 reads under different treatments. In addition, other star miRNAs expression levels were low under all four conditions, these were miR172b*, miR156h*, and miR166g*. Other studies showed that miRNAs are often evolutionarily conserved throughout the plants [25, 26]. Hence, we investigated the evolutionary conservation features of the identified miRNAs in soybean by comparing them to Arabidopsis thaliana, rice, Zea mays, Medicago truncatula, Sorghum bicolor, Triticum aestivum, Vitis vinifera, brassica and Pinus according to their sequence similarity (data not shown). The identified miRNA families are conserved in a variety of plant species. One hundred and ten miRNA genes were reported in Glycine max, the other 23 genes were detected from known orthologous miRNAs.
To validate the expression pattern of miRNAs by deep sequencing, we randomly selected ten miRNAs (miR156f, miR167d, miR169d, miR393a, miR394a, miR482, miR1507a, miR1508b, miR4369, and miR4397) to perform verification by qRT-PCR. Expression abundance patterns in three stress (drought, salinity, and alkalinity) induced samples were compared with the mock. Up-regulated miRNAs under three stress-induced conditions, which occurred most frequently with both methods, were miR167d, miR169d, miR482, miR1507a, miR1508b and miR4369. Only miR393a had shown to be not in accordance with Solexa result. MiR394a was down-regulated and exhibited an identical pattern in both methods. These highly concordant results between two methods suggest qRT-PCR validation indicated a good concordance of both methods (Figure 3).
Novel miRNAs in soybean
The top 10 novel miRNAs predicted from both arms of the miRNA precursor
Sequence of 5p
Sequence of 3p
In comparison with these conserved miRNAs, all the novel miRNA tags had low read counts in the four libraries, where the highest is only 4 830 at 5' end (Gma-001). The least is only one at 3' and 5' end (e.g. Gma-011, Gma-023, Gma-025, Gma-026, Gma-037, Gma-039, Gma-040, Gma-047), and the average read count was 318. It is well known that conserved miRNAs are highly expressed frequently and ubiquitously whereas non-conserved miRNAs are not. Further experimentation is needed to determine whether these novel miRNAs are stress induced.
MiRNAs expression patterns under drought, salinity, and alkalinity
The vast majority of the differentially expressed miRNAs showed different expression patterns either among three conditions or between two stress conditions. Of these, the expression of 78 miRNAs was significantly different (fold change > 2; p < 0.05) (Figure 4), these were congruously or differentially regulated under the three stress conditions. In three stress conditions, 27 miRNAs (e.g. miR1520d, miR1520n, and miR4407) were all up-regulated in comparison to the mock. For example, the expression level of miR4407 changed 3.67, 4.33, and 4.67 folds in drought, salinity, and alkalinity, respectively. Fifty-one miRNAs showed different trends under various inducing conditions (such as miR394a, miR4361, miR4396, and miR4308), indicating that individual miRNAs may have distinctive expression patterns under different stress conditions. For example, miR394a was up-regulated in drought (fold change = 2.09) but down-regulated in salinity (fold change = -8). Under different conditions, 70, 46 and 37 miRNAs were up-regulated with a fold change > 2 (e.g. miR169d), and 1, 4 and 8 were down-regulated with fold changes > -2 (e.g. miR393a) in drought, salinity and alkalinity, respectively. The expression profiles strongly indicate that different miRNA regulation patterns might completely or partly contribute to explaining the stress regulation between various treatments.
MiRNA targets prediction
Mature miRNA quantification by northern blotting
Nowadays, characterization of the vital roles of miRNAs play in plant stress responses is an active research field. Although many studies have demonstrated that plant miRNAs function as important regulators in development and morphogenesis processes, more reports are indicating that plant miRNAs are also involved in environmental stress tolerance .
Since abiotic stress is one of the primary causes of crop losses worldwide, unraveling the complex mechanisms underlying stress resistance of plants has profound significance. Recently, the newly developed sequencing technologies, such as the Illumina Genome Analyzer (GA), Roche/454 FLX system, and the ABI SOLiD system, show advances over traditional methods with improved throughput and dramatically reduced cost. Currently, applications of high-throughput sequencing technologies are arousing much research interest, such as identification of entire sets of miRNAs, which deliver new insights into the role of miRNAs in plant development, and stress related regulation. By using this method, a number of soybean miRNAs have been well annotated . Differing from microarray, high throughput sequencing allows us to comprehensively survey stress related miRNAs. To date, little is known about the functions of miRNAs in abiotic stress responses in Glycine max.
In this study, we sequenced and analyzed small RNAs of the soybean under three treatments based on deep sequencing. Investigation of the small RNAs showed that gma-miR1507a (936 627 sequence tags) was represented in our sequencing libraries. One hundred and thirty three known miRNAs and 50 novel miRNAs were obtained from next generation sequencing data. Through expression abundance of the miRNA repertoires under drought, salinity, and alkalinity stress conditions, many miRNAs were found to have a wide range of expression levels between libraries. This characteristic of variability in miRNA expression may be due to miRNA mature processing , and/or stress associated regulation [2, 36]. We envision that these miRNAs might have functional significance, suggesting they may participate in the plant stress response. Highly abundant miRNAs seem to exhibit similar conservation. For example, miR2188 and miR3522b exhibit high expression levels in all four libraries and are conserved across many species. Such observations support previous results that the most abundant miRNAs were phylogenetically conservative .
Both miRNAs and star miRNAs are generated from step-loop hairpin structures. MiRNAs are stable and participate in translational repression or cleavage of mRNA by binding or anchoring to the coding region of mRNA sequences . Khvorova et al.  inferred from the considerably low abundance of star miRNAs that these strands are typically destroyed when released from pre-miRNA stem. The low expression levels of star miRNA sequences, such as miR156d*, miR157b*, and miR3630*, further support the miRNA synthesis hypothesis. Next generation sequencing is a powerful tool in the detection of miRNA and star miRNA [[15, 39] and ]. The correlation between star miRNA and its flexible expression may reveal its particular regulated function. MiR162* and miR482* may be involved in regulating stress. Two arms of a single hairpin, giving rise to RNA function isolation by different sequences, may associate with distinct biological activities. Small novel miRNAs annotated in our study, such as the 5' and 3' of Gma-004 and Gma006, were derived from predicted hairpin structures.
Plant miRNAs have been reported as having a strong propensity towards regulating responses to abiotic stress, including dehydration, freezing, salinity, alkalinity, and other stresses by transcriptional factors or proteins . Expression levels of miRNAs induced by environmental stressors vary. They therefore may play a key role in targeting stress-regulated genes. It has been reported that stress response miRNAs were ubiquitously present in Populus , soybean , and other plants. Previous studies have reported that members of miR167, miR319, and miR393 were similarly regulated in stress tolerance [9, 42, 43]. In this study, members of miR1520n, miR4374b, and miR4396 were up-regulated simultaneously under three stresses, which implies that they might target genes that function as negative regulators of stress tolerance. In addition, it was previously reported that miR395 was previously reported to be up-regulated in a salt induced soybean line targeting sulfurylase and ASP1 genes under sulfate starvation conditions. Therefore, we speculate that miR395 might be involved in non-specific salt-induced responding pathways, such as the maintenance of energy supply [7, 13]. Moreover, miR166 is responsive to dehydration stress in barley , and it is abundant and up-regulated in soybean seedlings under dehydration conditions. MiR393a, targeting F-box proteins and a basic-helix-loop-helix family protein, was up-regulated in cold, dehydration, salt, or ABA stress , and down-regulated in soybean under alkaline stress. These responsive miRNAs are involved in post-transcriptional regulation during stress responsive processes.
Deep sequencing of the small RNA transcriptome yields an incredible amount of data, from which we can not only determine known miRNAs, but also successfully explore novel miRNAs with high accuracy and efficiency. First, in this study, we have identified 133 known and 50 novel miRNAs in Glycine max, which illustrates the diversity of miRNA expression in Glycine max, revealing the presence of more miRNAs than previously known. In addition, deep sequencing technologies in combination with bioinformatics analysis enabled us to profile the miRNA expression patterns for further miRNA functional insights, and to elucidate the underlying molecular mechanisms and diverse physiological pathways. Second, comparing miRNA expression profiles under various induced conditions, we found significant differences in miRNA regulation patterns, with 71, 50, and 45 altered expression patterns under drought, salinity, and alkalinity, respectively. The differentially expressed miRNAs obtained in this study can serve as a basis for further identification of the regulation roles of stress tolerance in Glycine max.
In this study, soybean miRNAs associated with stress responses (drought, salinity, and alkalinity) have been identified and analyzed in combination with deep sequencing technology and in-depth bioinformatics analysis. One hundred and thirty three conserved miRNAs representing 95 miRNA families were expressed in soybeans under three treatments. In addition, 71, 50, and 45 miRNAs are either uniquely or differently expressed under drought, salinity, and alkalinity, respectively, suggesting that many miRNAs are inducible and are differentially expressed in response to certain stress. This study has important implications for further identification of gene regulation under abiotic stresses and significantly contributes a complete profile of miRNAs in Glycine max.
Materials and methods
Sample collection and treatment
An inbred line of 'HJ-1', one of the abiotic stress sensitive soybeans, was used in our study. For each inbred line, the uniform seeds were treated with ethanol for 10 minutes and then rinsed several times with sterile distilled water. These seeds were cultured in 1 × Hoagland's nutrient solution (4 ml/L Fe-sequestrene, 6 mM K+ and 4 mM Ca2+). When the four leaf stage was reached, we began to put them under different stress treatments salt (120 mM NaCl), alkalinity (70 mM NaCl and 50 mM NaHCO3) and drought (2% PEG) stress) for 48 hours, with the unstressed plants as a mock. Then roots of 120 seedlings were collected and frozen in liquid nitrogen for later use.
Small RNA sequencing library construction
The isolated RNA samples were purified on 15% PAGE gel for size selection. Small RNAs, < 30 bases, were ligated with a pair of Solexa sequencing adaptor primers (5'-pUCGUAUGCCGUCUUCUGCUUGidT-3' and 5'-GUUCAGAGUUCUACAGUCCGACGAUC-3') using T4 RNA ligase. Ligated RNA was size-fractionated on a 10% agarose gel and the 70-90 nt fractions were amplified for 15 cycles to transform RNA to cDNA to produce sequencing libraries. The purified libraries with approximately 20 mg of small RNA were used for cluster generation and sequencing analysis using the Solexa sequencer (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. All the short reads were deposited in the National Center for Biotechnology Information (NCBI) and can be accessed in the Short Read Archive (SRA) under the accession number SRA045367.1.
After Solexa sequencing, high-quality small RNA reads were extracted from raw reads through filtering out the low quality tags and eliminating contamination of adaptor sequences. The resulted set of unique sequences with related read counts were deemed as clean sequence tags. Matched sequences were then queried against non-coding RNAs (rRNA, tRNA, snRNA, and snoRNA) from Rfam database using SOAP 2.0 program http://soap.genomics.org.cn/. Any small RNA read matches to these sequences were excluded from further analysis. Next, we aligned all sequences against the miRBase16.0 again http://mirbase.org/ using SOAP 2.0 to search for known miRNAs with allowed mismatches (or > 90% identity). To compare miRNA expression data under the four diverse treatments, initially, each identified miRNA read count was normalized to the total number of reads in each given sample. Then, Bayesian method was applied to evaluate the statistical significance (P value). After the Bayesian test, if the P value ≤ 0.01 and the normalized sequence counts changed more than two folds, the specific miRNA was considered to be differently expressed.
Reads that did not match any databases above were marked as unannotated. To identify novel miRNA prediction, small RNA tags that matched miRBase and Rfam were filtered and the remaining tags were aligned with the Glycine max genome. To analyze whether the matched sequence could form a suitable hairpin (the secondary structure of the small RNA precursor), sequences surrounding the matched sequence were extracted. The second structure was predicted by RNAfold http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi. Thereafter, novel miRNAs were identified using the MIREAP program developed by the BGI (Beijing Genome Institute, http://sourceforge.net/projects/mireap/) and mirTools . The miRNA candidate targets were predicted using psRNATarget http://bioinfo3.noble.org/psRNATarget/. The COG (Clusters of Orthologous Groups) terms of target genes were annotated by comparing with COGs from NCBI http://www.ncbi.nlm.nih.gov/COG.
MiRNA quantification by real-time PCR
Total RNA (1 μl) was used for synthetizing reverse transcripts with One Step PrimeScript® miRNA cDNA Synthesis Kit (Takara, Japan) in 20 μl reaction mixture. The reaction was performed at 37°C for 60 min, 85°C for 5 seconds following the manufacturer's instruction. RT-PCR was performed with SYBR® Premix Ex Taq II™ (Takara, Japan). Primers designed in Additional file 11 were used to amplify specific miRNA. Soybean 5s rRNA was used as the endogenous control. Uni-miR qPCR Primer was added as the common reverse primer. The qRT-PCR reactions were carried out in a final volume of 25 μl containing 12.5 μl SYBR® Premix EX Taq™, 1 μl forward and reverse primers, and 2 μl template. To estimate the relative abundance of miRNAs in stress induced samples, the Ct value was directly compared and transformed into a fold-change difference. These reactions were performed using the ABI7300 (Applied Biosystems 7300 Real-Time PCR System).
MiRNA verification by northern blot
For miRNAs quantification, northern blot hybridization was conducted using the High Sensitive MiRNA Northern Blot Assay Kit (Signosis, USA). 30 μg total RNA of each sample was electrophoresed on a 15% polyacrylamide gel and transferred to membrane (supplied by Signosis). Antisense RNA biotin labeled in the 5' end (Invitrogen, China) was used for hybridization probes. The Cyber Green® II-stained (Biotech, China) rRNA bands in the polyacrylamide gel are shown as a loading control.
This work was supported by grants from the Special Program for Research of Transgenic Plants (Grant Nos. 2008ZX08010-002, 2011ZX08010-002), the National Natural Science Foundation of China (Grant No. 30971804), the Program for New Century Excellent Talents in University (Grant No.NCET-08-0693) and the State Key Laboratory of Crop Biology at Shandong Agricultural University, China (grant no. 2010KF02).
- Boyer JS: Plant productivity and environment. Science. 1982, 218 (4571): 443-448.PubMedView ArticleGoogle Scholar
- Yamaguchi-Shinozaki K, Shinozaki K: Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annu Rev Plant Biol. 2006, 57: 781-803.PubMedView ArticleGoogle Scholar
- Bartel B, Bartel DP: MicroRNAs: at the root of plant development?. Plant Physiol. 2003, 132 (2): 709-717.PubMedPubMed CentralView ArticleGoogle Scholar
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297.PubMedView ArticleGoogle Scholar
- Lai EC: microRNAs: runts of the genome assert themselves. Curr Biol. 2003, 13 (23): R925-936.PubMedView ArticleGoogle Scholar
- Matzke M, Matzke AJ, Kooter JM: RNA: guiding gene silencing. Science. 2001, 293 (5532): 1080-1083.PubMedView ArticleGoogle Scholar
- Jones-Rhoades MW, Bartel DP: Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell. 2004, 14 (6): 787-799.PubMedView ArticleGoogle Scholar
- Phillips JR, Dalmay T, Bartels D: The role of small RNAs in abiotic stress. FEBS Lett. 2007, 581 (19): 3592-3597.PubMedView ArticleGoogle Scholar
- Gao P, Bai X, Yang L, Lv D, Pan X, Li Y, Cai H, Ji W, Chen Q, Zhu Y: osa-MIR393: a salinity- and alkaline stress-related microRNA gene. Mol Biol Rep. 2010.Google Scholar
- Trindade I, Capitao C, Dalmay T, Fevereiro MP, Santos DM: miR398 and miR408 are up-regulated in response to water deficit in Medicago truncatula. Planta. 2010, 231 (3): 705-716.PubMedView ArticleGoogle Scholar
- Zhu QH, Spriggs A, Matthew L, Fan L, Kennedy G, Gubler F, Helliwell C: A diverse set of microRNAs and microRNA-like small RNAs in developing rice grains. Genome Res. 2008, 18 (9): 1456-1465.PubMedPubMed CentralView ArticleGoogle Scholar
- Jin W, Li N, Zhang B, Wu F, Li W, Guo A, Deng Z: Identification and verification of microRNA in wheat (Triticum aestivum). J Plant Res. 2008, 121 (3): 351-355.PubMedView ArticleGoogle Scholar
- Ding D, Zhang L, Wang H, Liu Z, Zhang Z, Zheng Y: Differential expression of miRNAs in response to salt stress in maize roots. Ann Bot. 2009, 103 (1): 29-38.PubMedPubMed CentralView ArticleGoogle Scholar
- McCormick KP, Willmann MR, Meyers BC: Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments. Silence. 2011, 2 (1): 2.PubMedPubMed CentralView ArticleGoogle Scholar
- Song QX, Liu YF, Hu XY, Zhang WK, Ma B, Chen SY, Zhang JS: Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing. BMC Plant Biol. 2011, 11: 5.PubMedPubMed CentralView ArticleGoogle Scholar
- Szittya G, Moxon S, Santos DM, Jing R, Fevereiro MP, Moulton V, Dalmay T: High-throughput sequencing of Medicago truncatula short RNAs identifies eight new miRNA families. BMC Genomics. 2008, 9: 593.PubMedPubMed CentralView 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.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhou LL, Li XM, Liu Q, Zhao FQ, Wu JY: Small RNA transcriptome investigation based on next-generation sequencing technology. J Genet Genomics.
- Pandey SP, Shahi P, Gase K, Baldwin IT: Herbivory-induced changes in the small-RNA transcriptome and phytohormone signaling in Nicotiana attenuata. Proc Natl Acad Sci USA. 2008, 105 (12): 4559-4564.PubMedPubMed CentralView ArticleGoogle Scholar
- Vidal EA, Araus V, Lu C, Parry G, Green PJ, Coruzzi GM, Gutierrez RA: Nitrate-responsive miR393/AFB3 regulatory module controls root system architecture in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2010, 107 (9): 4477-4482.PubMedPubMed CentralView ArticleGoogle Scholar
- Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J: SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009, 25 (15): 1966-1967.PubMedView ArticleGoogle Scholar
- Kulcheski FR, de Oliveira LF, Molina LG, Almerao MP, Rodrigues FA, Marcolino J, Barbosa JF, Stolf-Moreira R, Nepomuceno AL, Marcelino-Guimaraes FC, et al: Identification of novel soybean microRNAs involved in abiotic and biotic stresses. BMC Genomics. 2011, 12: 307.PubMedPubMed CentralView ArticleGoogle Scholar
- Joshi T, Yan Z, Libault M, Jeong DH, Park S, Green PJ, Sherrier DJ, Farmer A, May G, Meyers BC, et al: Prediction of novel miRNAs and associated target genes in Glycine max. BMC Bioinformatics. 2010, 11 (Suppl 1): S14.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen R, Hu Z, Zhang H: Identification of microRNAs in wild soybean (Glycine soja). J Integr Plant Biol. 2009, 51 (12): 1071-1079.PubMedView ArticleGoogle Scholar
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science. 2001, 294 (5543): 853-858.PubMedView ArticleGoogle Scholar
- Floyd SK, Bowman JL: Gene regulation: ancient microRNA target sequences in plants. Nature. 2004, 428 (6982): 485-486.PubMedView ArticleGoogle Scholar
- Yang X, Zhang H, Li L: Global analysis of gene-level microRNA expression in Arabidopsis using deep sequencing data. Genomics. 2011, 98 (1): 40-46.PubMedView ArticleGoogle 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 (6): 960-976.PubMedGoogle Scholar
- Klevebring D, Street NR, Fahlgren N, Kasschau KD, Carrington JC, Lundeberg J, Jansson S: Genome-wide profiling of populus small RNAs. BMC Genomics. 2009, 10: 620.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang B, Pan X, Cobb GP, Anderson TA: Plant microRNA: a small regulatory molecule with big impact. Dev Biol. 2006, 289 (1): 3-16.PubMedView ArticleGoogle Scholar
- Matts J, Jagadeeswaran G, Roe BA, Sunkar R: Identification of microRNAs and their targets in switchgrass, a model biofuel plant species. J Plant Physiol. 2010, 167 (11): 896-904.PubMedView ArticleGoogle Scholar
- Carlsbecker A, Lee JY, Roberts CJ, Dettmer J, Lehesranta S, Zhou J, Lindgren O, Moreno-Risueno MA, Vaten A, Thitamadee S, et al: Cell signalling by microRNA165/6 directs gene dose-dependent root cell fate. Nature. 2010, 465 (7296): 316-321.PubMedPubMed CentralView ArticleGoogle Scholar
- Katara P, Gautam B, Kuntal H, Sharma V: Prediction of miRNA targets, affected proteins and their homologs in Glycine max. Bioinformation. 2010, 5 (4): 162-165.PubMedPubMed CentralView ArticleGoogle Scholar
- Severin AJ, Woody JL, Bolon YT, Joseph B, Diers BW, Farmer AD, Muehlbauer GJ, Nelson RT, Grant D, Specht JE, et al: RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome. BMC Plant Biol. 2010, 10: 160.PubMedPubMed CentralView ArticleGoogle Scholar
- Winter J, Jung S, Keller S, Gregory RI, Diederichs S: Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol. 2009, 11 (3): 228-234.PubMedView ArticleGoogle Scholar
- Sunkar R, Zhu JK: Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis. Plant Cell. 2004, 16 (8): 2001-2019.PubMedPubMed CentralView ArticleGoogle Scholar
- Sunkar R, Jagadeeswaran G: In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biol. 2008, 8: 37.PubMedPubMed CentralView ArticleGoogle Scholar
- Khvorova A, Reynolds A, Jayasena SD: Functional siRNAs and miRNAs exhibit strand bias. Cell. 2003, 115 (2): 209-216.PubMedView ArticleGoogle Scholar
- Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J, Bertone P, Caldas C: Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA. 2010, 16 (5): 991-1006.PubMedPubMed CentralView ArticleGoogle Scholar
- Evan Johnson W, Welker NC, Bass BL: Dynamic Linear Model for the Identification of miRNAs in Next-Generation Sequencing Data. Biometrics. 2011.Google Scholar
- Lu S, Sun YH, Shi R, Clark C, Li L, Chiang VL: Novel and mechanical stress-responsive MicroRNAs in Populus trichocarpa that are absent from Arabidopsis. Plant Cell. 2005, 17 (8): 2186-2203.PubMedPubMed CentralView ArticleGoogle Scholar
- Sunkar R, Chinnusamy V, Zhu J, Zhu JK: Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci. 2007, 12 (7): 301-309.PubMedView ArticleGoogle Scholar
- Girginova PI, Daniel-da-Silva AL, Lopes CB, Figueira P, Otero M, Amaral VS, Pereira E, Trindade T: Silica coated magnetite particles for magnetic removal of Hg(2+) from water. J Colloid Interface Sci. 2010.Google Scholar
- Kantar M, Unver T, Budak H: Regulation of barley miRNAs upon dehydration stress correlated with target gene expression. Funct Integr Genomics. 2010, 10 (4): 493-507.PubMedView ArticleGoogle Scholar
- Zhu E, Zhao F, Xu G, Hou H, Zhou L, Li X, Sun Z, Wu J: mirTools: microRNAprofiling and discovery based on high-throughput sequencing. NucleicAcids Res 2010, 38 Web Server: W392-397.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.