Validation and characterization of Citrus sinensis microRNAs and their target genes
- Changnian Song†1,
- Mingliang Yu†2,
- Jian Han1,
- Chen Wang1,
- Hong Liu1,
- Yanping Zhang1 and
- Jinggui Fang1Email author
© Song et al.; licensee BioMed Central Ltd. 2012
Received: 6 January 2012
Accepted: 4 April 2012
Published: 15 May 2012
MicroRNAs play vital role in plant growth and development by changeable expression of their target genes with most plant microRNAs having perfect or near-perfect complementarities with their target genes but miRNAs in Citrus sinensis (csi-miRNAs) and their function have not been widely studied.
In this study, 15 potential microRNAs in Citrus sinensis (csi-miRNAs) were identified and bioinformatically validated using miR-RACE, a newly developed method for determination of miRNAs prediction computationally. The expression of these fifteen C. sinensis miRNAs can be detected in leaves, stems, flowers and fruits of C. sinensis by QRT-PCR with some of them showed tissue-specific expression. Six potential target genes were identified for six csi-miRNAs and also experimentally verified by Poly (A) polymerase -mediated 3′ rapid amplification of cDNA ends (PPM-RACE) and RNA ligase-mediated 5′ rapid amplification of cDNA ends (RLM-RACE) which mapped the cleavage site of target mRNAs and detected expression patterns of cleaved fragments that indicate the regulatory function of the miRNAs on their target genes.
Our results confirm that small RNA-mediated regulation whereby all csi-miRNAs regulate their target genes by degradation.
MicroRNAs (miRNAs) are small, single-stranded, non-protein coding RNAs of ~21 nt in length, present in plants and animals which regulating the gene expression at the posttranscriptional levels by binding to target mRNAs for mRNA cleavage or inhibition of mRNA translation. miRNA play an important role in the plant kingdom are evolutionarily conserved from mosses and ferns to higher flowering plants. This conservation has been used as a powerful strategy for identification or prediction of miRNAs by homology searches in other species [1, 2]. Sunkar et al  reported that 682 miRNAs have been identified in 155 diverse plant species and they found more than 15 conserved miRNA families in eleven 11 plant species by searching all publicly available nucleotide databases. In prior studies, combination of computational prediction and experimental verification was used to identify miRNAs, with experimental validation mainly focused on determining the expression of miRNAs using robust techniques of RNA blotting and/or RT-PCR. However, these two techniques can only confirm existence and size, but not the full precise sequences, especially termini nucleotides computationally predicted miRNAs .
In addition to the economic value of citrus, availability of a large number of expressed sequence tags (ESTs) in citrus makes it an excellent source of experimental material for elucidation of gene expression and regulation. miRNAs have been extensively studied in the modern research but systematic study has not been performed on leaf, stem, flower, and fruit growth and development of C. sinensis.. Recently, three study groups identified several miRNAs from citrus using computational approaches and deep sequencing [1, 3, 5, 6], but the number of C. sinensis miRNAs predicted still remains low, and experimental validation hardly carried out. In this study, precise sequences, especially terminal nucleotides of 15 csi-miRNAs were validated by miR-RACE, a recent technique developed by Song et al.  based on 17 miRNAs from 13 miRNA families that were computationally predicted in our previous study. The ubiquitous expression of these 15 miRNAs can be detected in different tissue of citrus by QRT-PCR. For detection of miRNA-mediated cleavage products, and determination of the manner of miRNA regulation on target genes, Poly (A) polymerase -mediated 3′ rapid amplification of cDNA ends (PPM-RACE) and RNA ligase-mediated 5′ rapid amplification of cDNA ends (RLM-RACE) developed. The result suggested that the importance of miRNAs in regulating development of C. sinensis and indicate that comprehensive studies of miRNAs in citrus would facilitate further understanding of regulatory mechanisms behind floral induction, stage transition and organ genesis. The new strategy developed here can definitely facilitate ease of studying the mechanisms of miRNA regulation on their target genes.
Precise sequence validation of csi-miRNAs
In our study we have detected the new 17 csi-miRNAs , belonging to be 13 families from C. sinensis genome by computational screening and it is the first time being reported in China. With conserved complementarities to miRNAs in other plant species and one important member of each family were successfully verified. The difference between our method and traditional RACE lies in the gene-specific primers used (Additional file 1). The PCR products were cloned and sequenced when they yielded reliable band. Sequences of each pair of 5′ and 3′ PCR products in each of the 15 csi-miRNAs were spliced to generate whole mature miRNA sequences (Additional file 2).
List of computer predicted and verified miroRNA in C. sinensis
Predicted miRNA sequence
Verified miRNA sequence
Expression analysis of csi-miRNAs by QRT-PCR
In summary, 15 potential C. sinensis miRNAs, all identified by miR-RACE were validated by QRT-PCR with some being expressed ubiquitously in all tissues with tissue and/or growth stage specific characteristics reflected at different expression levels. This can be an essential reference for comprehensive analysis of the function of these csi-miRNAs.
Potential target prediction for fifteen C. sinensis miRNA
The potential miRNA target genes of several conserved C. sinensis miRNAs, such as UC52-29592 (Auxin response factor 10, ARF10) for miR160, UC52-35004, UC52-31207, and UC52-10373 (Homeo domain leucine zipper, HD-Zip protein) for miR165, UC52-31207, and UC52-10373 (HD-Zip protein) for miR166, UC52-24193 (AP2) for miR172 (Additional file 4: Table S3). No targets were found in C. sinensis for csi-miR162, csi-miR530, csi-miR844, csi-miR950, csi-miR1027, csi-miR1044-3p, and csi-miR1426but important targets included NB-LRR disease resistance gene analogs, such as UC52-75213 that was similar to P. trichocarpa XM_002298664 cc-nbs-lrr resistance protein (Additional file 4).
miRNA target gene expression analysis
Identification of miRNA-guided cleavage of target mRNAs in C. sinensis miRNAs
Detection of miRNA cleaved target mRNAs by PPM-RACE, RLM- RACE
miRNAs regulate their target genes as well as the degradation levels of the target mRNAs, we first employed a new and efficient strategy that integrates preparation of cleaved target miRNAs, QRT-PCR amplification of 3′ and 5′ end fragments of the cleaved products, sequence-directed cloning, and alignment analysis (Additional file 5). Even though the quantity of the 5′ ends has been reported to be lower than that of the corresponding 3′ ends of the phase , and despite the 5′ and 3′ ends of the target gene theoretically showing relatively consistent trends, it was necessary to know the information of both the 3′ and 5′ ends from the cleaved mRNA for comprehensive understanding of regulation of miRNAs on their target genes. The existing levels of six group cleaved by the six miRNAs exhibited similar patterns of expression (Additional file 5), with their corresponding miRNAs. Majority of the miRNA cleaved target mRNAs existed ubiquitously in all tissues, and some showed tissue-, and/or growth-stage-specific expression patterns.
Although plant miRNAs have been studied in the recent past, but few studies have been conducted on C. sinensis, a most important fruits crops in the world. Sunkar and Jagadeeswaran  identified several miRNAs from citrus EST by bioinformatics, but C. sinensis miRNAs still remain largely unknown and few the identified C. sinensis miRNAs have not yet been experimentally confirmed. To elucidate the functions and the regulation pathways of miRNAs in citrus, it is important to know the localization of specific miRNAs. Although a numbers of methods have been developed for computational prediction of miRNAs, their disadvantages have not been overcome experimentally. These disadvantages include the aspect whereby precise sequences of miRNAs are seldom determined and where several candidate miRNA orthologs or paralogs might be predicted for a specific miRNA (e.g. the prediction of csi-miR482). We report the use of miR-RACE as the first experimental approach to overcome this problem, through employment of challenging steps.
Reverse transcription followed by quantitative PCR analysis (QRT-PCR) with nonspecific double-stranded DNA-binding fluorophores, such as SYBR Green, is a powerful alternative for highly sensitive, rapid, multi-parallel, and cost-effective expression analysis . Shi and Chiang  and Chen et al.  reported two QRT-PCR-based methods to measure the levels of mature miRs. The first approach relies on in vitro polyadenylation of mature miRs followed by RT with an oligo(dT) adapter primer and amplification using SYBR Green with a miR-specific forward primer and a compatible reverse primer. In the second approach, each specific miR is reverse transcribed from total RNA using a specific stem-loop primer, followed by TaqMan PCR amplification. Although it is desirable to quantify the biologically active mature miR species, a limitation of both QRT-PCR methods is that they are unable to differentiate the expression strengths of MIRNA genes that yield (nearly) identical mature miR molecules. Varkonyi-Gasic et al.  describe and provide protocols for an end-point and real-time looped RT-PCR procedure. This method enables fast, sensitive and specific miRNA expression profiling and is suitable for facilitation of high throughput detection and quantification of miRNA expression.
Although miRNAs generally function as negative regulators of gene expression by mediating the cleavage of target mRNAs  or by repressing their translation , the cleavage of target mRNAs appears to be predominant mode in gene regulation by plant miRNAs . Finding the cleavage site supposedly located in the sequence complementary to the miRNA in the target gene is essential for verification of the cleavage of target genes. Among the methods used to observe miRNA-dependent cleavage of targets, RLM-RACE and PARE is the most useful. PARE using a second sequencing technology (e.g. Illumina-Solexa) is another new approach being adopted for the analysis of miRNAs targets [17–20]. The high number of sequence reads promises sensitivity, yet the necessary expertise required and the labor and cost involved are considerable. In this study, we developed a combination of PPM-RACE and RLM-RACE for mapping miRNA-mediated cleavage products and validation of miRNA targets, which takes advantage of modified 5′ rapid and 3′ rapid amplification of cDNA ends as well as QRT-PCR and bioinformatic tools. The PPM-RACE and RLM-RACE was done on unigenes for detection and cloning of the mRNA fragments which correspond precisely to the predicted products of miRNA processing.
We performed the PPM-RACE and RLM-RACE on unigenes so as to detect and clone the mRNA fragments corresponding precisely to the predicted products of miRNA processing. The findings from this PPM-RACE and RLM-RACE could be used as a criterion for confirming the putative targets. In total, we performed PPM-RACE and RLM-RACE assays on six predicted target genes (UC52-29592, UC52-35004, UC52-31207, UC52-10373, UC52-24193, and UC52-75213) which are representative targets of five conserved miRNAs (miR160, miR165, miR166, miR172, and miR482a, respectively). Unigene C46-29592 comprises of auxin response factors (ARFs) that bind to auxin response elements in promoters of early auxin response genes . UC52-35004, UC52-31207, and UC52-10373 were all conversed Class-III homeodomain leucine zipper (HD-ZIP) proteins. Another experimentally confirmed miRNA-targeted transcription factor is APETALA2 (AP2), which controls floral development and phase transition in Arabidopsis . UC52-10373 is similar to Arabidopsis proteins coded by IRX12 copper ion binding/ oxidoreductase (IRX12CBO), which coded for a protein highly homologous to NB-LRR disease resistance protein. Targeting of NB-LRR genes by miRNAs has previously been reported in poplar, Arabidopsis and grape [22–24], but its contribution to disease resistance is still poorly characterized. In this research, UC52-24193, which is predicted target of miR172, was found to be a member of the AP2 gene family. All four predicted targets were found to have specific cleavage sites corresponding to the miRNA complementary sequences (Figure 3), and the most common 5′ end of the mRNA fragments mapped to the nucleotides that pair with the 10th miRNA nucleotide from the 5′ ends. miRNAs may directly target transcription factors that affect plant development and also specific genes that control metabolism. In our study, it appears that our predicted targets play role not only in development, but also in diverse physiological processes. In summary, the efficient and powerful approach developed herein can be successfully used to validate the expression of csi-miRNA cleaved target mRNAs.
This is the first time we discovered precise sequences of fifteen csi-miRNAs through miR-RACE. In addition, expression of these 15 C. sinensis miRNAs can be detected by QRT-PCR in young, mature and old leaves, young, mature and old stems, flower buds, half open flowers, open flowers, and developing fruits at 15, 45, 75, 105 and 145 DAFB, along with csi-miRNAs showing tissue-specific expression. And six potential target genes for six csi-miRNAs were experimentally verified by PPM-RAC and RLM-RACE. These results showed that regulatory miRNAs exist in agronomically C. sinensis and play an essential role in citrus growth, development, and response to disease.
Materials and methods
Samples were collected from two five-year-old ‘Newhall’ navel oranges (Citrus sinensis [L.] Osbeck) trees grown at the Suzhou Evergreen Fruit Tree Research Institute, China. Young, mature and old leaves and stems, flower buds, half open and fully open flowers and fruits at different stages of development (15, 45, 75, 105 and 145 days after full bloom, DAFB) were collected. After collection, all samples were immediately frozen in liquid nitrogen and stored at −80°C.
C. sinensis miRNAs and prediction of potential target mRNAs
To validate bioinformatically predicted miRNAs, 17 mature miRNA sequences (csi-miR160, csi-miR162, csi-miR165, csi-miR166a, csi-miR166b, csi-miR172, csi-miR390, csi-miR482a.1, csi-miR482a.2, csi-miR482a.3, csi-miR482a.4, csi-miR530, csi-miR844, csi-miR950, csi-miR1027, csi-miR1044-3p, and csi-miR1426) were downloaded from the miRNA Registry database (Release 15.0, May 2010; http://microrna.sanger.ac.uk) and from previous publications .
Putative C. sinensis miRNAs were blasted against Harvest C52 Citrus unigene database on the Harvest Blast Search web server (http://18.104.22.168/blast/index.html). BLASTn hits with less than four nucleotide mismatches (plus/minus) were chosen as candidate targets, and were then searched in the Citrus Harvest 1.20 program using BLASTx to obtain their putative functions. Meanwhile, citrus miRNAs were blasted with citrus ESTs by Blast 2.17 (Elue set as 10). ESTs with less than four nucleotide mismatches (plus/minus) were extracted and then annotated by online BLASTx search against SwissProt protein sequences (SwissProt) or nondundant protein sequence (nr) database on the NCBI web server.
Low molecular weight RNA extraction and construction of small RNA cDNA libraries
Total RNA isolated from 100 mg of previously collected tissue using TRIZOL reagent (Invitrogen, Life Technologies, Carlsbad, CA). Low and high molecular weight RNAs (LMW and HMW RNA) were separated with 4 M LiCl [2, 24]. The procedure of construction of small RNA cDNA libraries as used by Fu et al.  was followed to generate the miRNA-enriched library.
Verification of csi-miRNAs precise sequence by miR-RACE
In validation of csi-miRNAs using miR-RACE , cDNA libraries from the small RNA pool of RNA samples isolated from various organs and tissues was amplified with mirRacer 5′ primer (5′-GGACACTGACATGGACTGAAGGAGTA-3′) and mirRacer 3′ primer (5′-ATTCTAGAGGCCGAGGCGGCCGACATG-3′) to generate a pool of non-gene-specific products. GSP1 and GSP2 were complementary to seventeen nucleotide length sequences of potential csi-miRNAs and a part of Poly (T) and 5′ adaptor (Additional file 1). In each case, a unique gene-specific DNA fragment was amplified. 5′ and 3′ end clones with PCR products of about 56 bp and 87 bp in length, respectively, were sequenced .
Real-Time PCR of C. sinensis miRNAs and their target mRNAs
To amplify csi-miRNAs from the reverse transcribed cDNAs, we used precise sequences of the csi-miRNAs as the forward primers (Additional file 1) and the mirRacer 3′ Primer as the reverse primer . RT-PCR was conducted with Rotor-Gene 3000 (Corbett Robotics, Australia) and the Rotor-Gene software version 6.1 . Comparative quantification was used to determine relative expression levels as previously described . 5.8 S rRNA used as a reference gene in the qPCR detection of miRNAs like in Arabidopsis and data analyzed with an R2 above 0.998 using LinRegPCR program  and expression of experimentally determined and predicted target genes assayed by QRT-PCR as previously described by Wilson et al. . The reverse transcription product was amplified using gene-specific primers that overlapped known or predicted cleavage sites (Additional file 6). Reactions were performed in triplicate on a Rotor-Gene 3000 (Corbett Robotics, Australia). Data was normalized to AT5G08290  and analyzed using a comparative quantification procedure .
Mapping of mRNA cleavage sites with PPM-RACE and RLM-RACE
To map miRNA-mediated cleavage products, and to determine the manner by which the miRNAs regulate their target genes, the new strategy developed in this study and appropriately named PPM-RACE and RLM-RACE, was used. The method comprises of the following main steps: HMW RNAs polyadenylated at 37°C for 60 min in a 50 μl reaction mixture with 5 μg of HMW RNAs, 1 mM ATP, 2.5 mM MgCl2, and 8 U poly (A) polymerase (Ambion, Austin, TX), and were ligated to a 5′ adapter (5′-CGACUGGAGCACGAGGACACUGACAUGGACUGAAGGA GUAGAAA-3′) using T4 RNA ligase (Invitrogen, Carlsbad, CA), respectively. Poly (A)-tailed HMW RNA recovered by phenol/chloroform extraction and ethanol precipitation. Poly (A)-tailed HMW RNA and adapter-ligated HMW RNA was recovered by phenol/chloroform extraction followed by ethanol precipitation. Reverse transcription was performed as done by Fu et al.. PPM-RACE and RLM-RACE amplifications were performed according to the GeneRacer kit guide (Invitrogen). Gene-specific PPM-RACE and RLM-RACE reactions were performed with the GeneRacer Nested Primer and gene-specific primers as shown in Additional file 7 and Additional file 8. Amplification products were gel purified, cloned, and sequenced, and at least fifteen independent clones were sequenced.
This work was supported by the NCET Program of China (Grant No. NCET-08-0796) and the Science & Technology Key Project of the China Ministry of Education (Grant No. 109084).
- Zhang BH, Pan XP, Cannon CH, 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.PubMedView ArticleGoogle Scholar
- Song C, Fang J, Li X, Liu H, Chao Thomas C: Identification and characterization of 27 conserved microRNAs in citrus. Planta. 2009, 230: 671-685. 10.1007/s00425-009-0971-x.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: 37-10.1186/1471-2229-8-37.PubMedPubMed CentralView ArticleGoogle Scholar
- Song C, Fang J, Wang C, Guo L, Nicholas KK, Ma Z: miR-RACE, a new efficient approach to determine the precise sequences of computationally identified trifoliate orange (Poncirus trifoliata) microRNAs. PLoS One. 2010, 5: e10861-10.1371/journal.pone.0010861.PubMedPubMed CentralView ArticleGoogle Scholar
- Sunkar R, Zhou X, Zheng Y, Zhang W, Zhu JK: Identification of novel and candidate miRNAs in rice by high throughput sequencing. BMC Plant Biol. 2008, 8: 25-10.1186/1471-2229-8-25.PubMedPubMed CentralView ArticleGoogle 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.PubMedPubMed CentralView ArticleGoogle Scholar
- Song C, Jia Q, Fang J, Li F, Wang C, Zhang Z: Computational identification of citrus microRNAs and target analysis in citrus expressed sequence tags. Plant Biology. 2010, 12: 927-934. 10.1111/j.1438-8677.2009.00300.x.PubMedView ArticleGoogle Scholar
- Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, Lao KQ, Livak KJ, Guegler KJ: Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005, 33: e179-10.1093/nar/gni178.PubMedPubMed CentralView ArticleGoogle Scholar
- Elbashir SM, Lendeckel W, Tuschl T: RNA interference is mediated by 21-and 22-nucleotide RNAs. Gen Dev. 2001, 16: 1616-1626.Google Scholar
- Kasschau KD, Xie Z, Allen E, Llave C, Chapman EJ, Krizan KA, Carrington JC: P1/HC-Pro, a viral suppressor of RNA silencing, interferes with Arabidopsis development and miRNA function. Dev Cell. 2003, 4: 205-217. 10.1016/S1534-5807(03)00025-X.PubMedView ArticleGoogle Scholar
- Chen X: MicroRNA Metabolism in Plants. Current Topics in Microbiology and Immunology. 2008, 320: 117-136. 10.1007/978-3-540-75157-1_6.PubMedPubMed CentralGoogle Scholar
- Udvardi MK, Czechowski T, Scheible WR: Eleven golden rules of quantitative RT-PCR. Plant Cell. 2008, 20: 1736-1737. 10.1105/tpc.108.061143.PubMedPubMed CentralView ArticleGoogle Scholar
- Shi R, Chiang VL: Facile means for quantifying microRNA expression by real-time PCR. Biotechniques. 2005, 39: 519-525. 10.2144/000112010.PubMedView ArticleGoogle Scholar
- Varkonyi-Gasic E, Wu R, Wood M, Walton EF, Hellens RP: Protocol: a highly sensitive RT-PCR method for detection and quantification of microRNAs. Plant Methods. 2007, 3: 12-10.1186/1746-4811-3-12.PubMedPubMed CentralView ArticleGoogle 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: 2053-2056. 10.1126/science.1076311.PubMedView ArticleGoogle Scholar
- Chen X: A microRNA as a translational repressor of APETALA2 in Arabidopsis flower development. Science. 2004, 303: 202-225. 10.1126/science.1090300.View ArticleGoogle Scholar
- Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ: Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol. 2008, 18 (Addo-Quaye C): 758-762.PubMedPubMed CentralView ArticleGoogle Scholar
- German MA, Pillay M, Jeong DH, Hetawal A, Luo S, Janardhanan P, Kannan V, Rymarquis LA, Nobuta K, German R, De Paoli E, Lu C, Schroth G, Meyers BC, Green PJ: Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol. 2008, 26: 941-946. 10.1038/nbt1417.PubMedView ArticleGoogle Scholar
- Gregory BD, O’Malley RC, Lister R, Urich MA, Tonti-Filippini J, Chen H, Millar AH, Ecker JR: A link between RNA metabolism and silencing affecting Arabidopsis development. Dev Cell. 2008, 14: 854-866. 10.1016/j.devcel.2008.04.005.PubMedView ArticleGoogle Scholar
- Li YF, Zheng Y, Addo-Quaye C, Zhang L, Saini A, Jagadeeswaran G, Axtell MJ, Zhang W, Sunkar R: Transcriptome-wide identification of microRNA targets in rice. Plant J. 2010, 62: 742-759. 10.1111/j.1365-313X.2010.04187.x.PubMedView ArticleGoogle Scholar
- Guilfoyle TJ, Hagen G: Auxin response factors. J Plant Growth Regul. 2001, 10: 281-291.View ArticleGoogle Scholar
- Lu C, Tej SS, Luo S, Haudenschild CD, Meyers BC, Green PJ: Elucidation of the small RNA component of the transcriptome. Science. 2005, 309: 1567-1569. 10.1126/science.1114112.PubMedView ArticleGoogle Scholar
- Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, Carrington JC: Highroughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One. 2007, 2: e219-10.1371/journal.pone.0000219.PubMedPubMed CentralView ArticleGoogle Scholar
- Adai A, Johnson C, Mlotshwa S, Archer-Evans S, Manocha V, Vance V, Sundaresan V: Computational prediction of miRNAs in Arabidopsis thaliana. Genome Res. 2005, 15: 78-91. 10.1101/gr.2908205.PubMedPubMed CentralView ArticleGoogle Scholar
- Fu H, Tie Y, Xu C, Zhang Z, Zhu J, Shi Y, Jiang H, Sun Z, Zheng X: Identification of human fetal liver miRNAs by a novel method. FEBS Lett. 2005, 579: 3849-3854. 10.1016/j.febslet.2005.05.064.PubMedView ArticleGoogle Scholar
- Wang JF, Zhou H, Chen YQ, Luo QJ, Qu LH: Identification of 20 microRNAs from Oryza sativa. Nucleic Acids Res. 2004, 32: 1688-1695. 10.1093/nar/gkh332.PubMedPubMed CentralView ArticleGoogle Scholar
- Wilson DN, Chung H, Elliott RC, Bremer E, George D, Koh S: Microarray analysis of postictal transcriptional regulation of neuropeptides. J Mol Neurosci. 2005, 25: 285-298. 10.1385/JMN:25:3:285.PubMedView ArticleGoogle Scholar
- Ramakers C, Ruijter JM, Deprez RH, Moorman AF: Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neuroscience Lett. 2003, 339: 62-66. 10.1016/S0304-3940(02)01423-4.View ArticleGoogle Scholar
- Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR: Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005, 139: 5-17. 10.1104/pp.105.063743.PubMedPubMed CentralView ArticleGoogle Scholar
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.