Identification of Schistosoma mansoni microRNAs
© Simões et al; licensee BioMed Central Ltd. 2011
Received: 31 July 2010
Accepted: 19 January 2011
Published: 19 January 2011
MicroRNAs (miRNAs) constitute a class of single-stranded RNAs which play a crucial role in regulating development and controlling gene expression by targeting mRNAs and triggering either translation repression or messenger RNA (mRNA) degradation. miRNAs are widespread in eukaryotes and to date over 14,000 miRNAs have been identified by computational and experimental approaches. Several miRNAs are highly conserved across species. In Schistosoma, the full set of miRNAs and their expression patterns during development remain poorly understood. Here we report on the development and implementation of a homology-based detection strategy to search for miRNA genes in Schistosoma mansoni. In addition, we report results on the experimental detection of miRNAs by means of cDNA cloning and sequencing of size-fractionated RNA samples.
Homology search using the high-throughput pipeline was performed with all known miRNAs in miRBase. A total of 6,211 mature miRNAs were used as reference sequences and 110 unique S. mansoni sequences were returned by BLASTn analysis. The existing mature miRNAs that produced these hits are reported, as well as the locations of the homologous sequences in the S. mansoni genome. All BLAST hits aligned with at least 95% of the miRNA sequence, resulting in alignment lengths of 19-24 nt. Following several filtering steps, 15 potential miRNA candidates were identified using this approach. By sequencing small RNA cDNA libraries from adult worm pairs, we identified 211 novel miRNA candidates in the S. mansoni genome. Northern blot analysis was used to detect the expression of the 30 most frequent sequenced miRNAs and to compare the expression level of these miRNAs between the lung stage schistosomula and adult worm stages. Expression of 11 novel miRNAs was confirmed by northern blot analysis and some presented a stage-regulated expression pattern. Three miRNAs previously identified from S. japonicum were also present in S. mansoni.
Evidence for the presence of miRNAs in S. mansoni is presented. The number of miRNAs detected by homology-based computational methods in S. mansoni is limited due to the lack of close relatives in the miRNA repository. In spite of this, the computational approach described here can likely be applied to the identification of pre-miRNA hairpins in other organisms. Construction and analysis of a small RNA library led to the experimental identification of 14 novel miRNAs from S. mansoni through a combination of molecular cloning, DNA sequencing and expression studies. Our results significantly expand the set of known miRNAs in multicellular parasites and provide a basis for understanding the structural and functional evolution of miRNAs in these metazoan parasites.
Small non-coding RNAs are increasingly providing insights into important aspects of the biology of many organisms [1, 2]. They include small interfering RNAs (siRNAs) and microRNAs (miRNAs), which are hallmarks of two important processes involved in RNA silencing [3, 4]. RNA silencing is a general process in which small RNA molecules derived from precursor dsRNA molecules trigger sequence-specific repression of gene expression [4–6].
miRNAs comprise a family of non-coding RNAs with approximately 21-25 nucleotides that down-regulate gene expression at the post-transcriptional level. miRNAs are generated from endogenous hairpin structures in the nucleus and play an important role in controlling diverse cellular functions in eukaryotes, including cell differentiation, development, apoptosis, and genome integrity [7–9]. In vivo experiments indicate a crucial role in cell proliferation and cell death processes for some miRNAs, including lin-4 and let-7 in C. elegans; bantam and mir-14 in Drosophila; and mir-23 in humans .
The current understanding of miRNA biogenesis involves a series of coordinated processes. Briefly, primary transcripts of miRNAs are processed in the nucleus by Drosha, an RNase III-like enzyme into pre-miRNA, which are first exported into the cytoplasm by exportin-5 and then processed into miRNAs by Dicer, another type III RNase [11–13].
The primary method of identifying miRNA genes has been to isolate, reverse transcribe, clone, and sequence small RNA molecules [14–16]. In animals, discovery of miRNA genes, by using molecular cloning based methods has been supplemented by systematic computational approaches that identify evolutionarily conserved miRNA genes. Bioinformatics tools search for patterns of sequence and secondary structure conservation that are characteristic of metazoan miRNA hairpin precursors [17–19]. However, considerable filtering must be performed to elucidate likely miRNA candidates. The 5' end of miRNAs is reported to have a perfect base alignment of at least 7 consecutive nucleotides, which enables their identification . The most sensitive of these methods indicate that miRNAs constitute nearly 1% of all predicted genes in nematodes, flies, and mammals [19–21]. However, computationally predicted miRNAs must be experimentally confirmed.
Although the first miRNA was identified in 1993, it was not until 2001 that the breadth of the miRNA gene class was recognized with cloning and sequencing of more than one hundred miRNAs from worms, humans, mice, and other species [22, 23]. However, no large-scale identification of miRNAs has been carried out in Schistosoma mansoni.
Schistosoma mansoni is a human parasite that is responsible for the neglected tropical disease schistosomiasis. The parasite infects approximately 90 million people worldwide, causing morbidity and eventually death in Central and South America and Africa . Although schistosomicidal drugs and other control measures exist, the development of new control strategies is necessary. In recent years, increasing attention has emerged over siRNAs as therapeutical agents . The emergence of gene ablation technologies based on the RNAi phenomenon has opened up new experimental opportunities. Recently, several reports on the use of RNAi for the studies of schistosomes were published [26, 27]. In this context, we attempted to identify potential miRNAs in S. mansoni. We use complementary experimental and computational approaches. We developed a homology-filtering approach used in a high-throughput pipeline in which all known miRNA genes were used as reference miRNAs. Fifteen potential miRNA candidates were discovered in S. mansoni using this analysis. The pipeline automated some of the manual steps, in particular a rule-based filtering approach for extracting the candidate pre-miRNA sequence, and it can also be applied to other genomes. By sequencing small-RNA cDNA libraries, we provide experimental evidence for 211 potential miRNAs candidates. The identification of new miRNA in the S. mansoni genome presents relevant information that is likely to be important for parasite development and sexual maturation.
Results and Discussion
Experimental identification of miRNAs
Cloning of short RNAs from S. mansoni adult worm pairs
An adult worm cDNA library of small RNAs was constructed using an established method based on a sequential ligation of oligonucleotide adapters to a size-fractionated sample of small RNAs . Concatenated DNA fragments (each fragment from one putative miRNA) were cloned into a plasmid vector to generate a library. A total of 582 recombinant clones randomly selected from the library were sequenced. Twelve hundred sequences were analyzed and show to contain ~2-3 small RNA sequence in the same vector. Size distribution of the non-redundant miRNA set ranged from 17 to 25 nt, although the majority contained 20-24 nt, 21 nt being the most abundant. To identify the putative origin of the cloned sequences, a FASTA search was performed against GenBank http://www.ncbi.nlm.nih.gov and the S. mansoni genome (version 4.0) . Sequences that had significant homology to breakdown products of abundant non-coding (nc) RNAs such as rRNA and tRNA were eliminated. A total of 584 ncRNAs were grouped into 211 clusters and were identified as possible miRNA candidates (see additional file 1, Table S1: clustering of 584 sequenced miRNAs). One hundred and sixty-one miRNAs were represented in the library by only one read and 50 were represented by clusters with up to 32 sequences. Since miRNAs are believed to occur at a frequency of approximately 0.5-1.5% of the total genes in the genome, the 13,200 genes predicted for S. mansoni should have generated between 66 and 198 miRNAs [30, 31]. Thus, the number of miRNAs experimentally observed is in the expected range.
Small S. mansoni RNAs with matches to know microRNAs present in the miRBase.
S. mansoni miRNAs
Number of Hits
Best Hit Name
Expression analysis of miRNAs in S. mansoni
Sequence analysis indicated miR-1 as the most abundant miRNA (32 reads). Although sequencing-based miRNAs expression profiling is a tool for measuring the relative abundance of miRNAs, the expression of the miR-1 was not detected by northern blot. In contrast, miR-32 is represented only by 2 clones in our sequences, which indicates a 12-fold lower expression level compared to that of miR-3 (24 reads). However, our small RNA blot analysis indicated that miR-32 was more abundantly expressed than miR-3 in the adult worm stage (Figure 1). The discrepancies between the cloning frequency and small RNA blot results could not be attributed to variations in RNA content because the same RNA samples were used for both experiments. One possible explanation could be bias in cloning efficiencies, or differential turnover rates of these miRNAs .
Computational identification of miRNAs
The high-throughput homology search pipeline was performed with all known miRNAs in miRBase (release 13.0). In total, 6,211 mature miRNAs were used as reference sequences. The e-value cutoff for this analysis was set at 0.01. A total of 180 hits were registered. We observed 110 unique S. mansoni sequences, and 15 sequences were represented multiple times. For the BLASTn results see additional file 3, Table S2: high-throughput pipeline homology search results. The existing mature miRNAs that produced these hits are reported, as well as the locations of the homologous sequences in the S. mansoni genome. All hits aligned with at least 95% of the miRNA sequence, resulting in alignment lengths of 19-24 nt. All of the 110 unique mature miRNA candidates returned by the BLASTn search were assigned an analysis identifier with prefix 'SMan'.
The extended sequences (with additional 50 nt flanking the mature sequence) were folded with RNAshapes . For the complete results for the extended sequence folding see additional file 4, Table S3: high-throughput pipeline extended sequence folding results.
Mean free energy (MFE) is a widely used criterion for filtering RNA folding results, and was observed to be an important filtering step in this analysis as well. The rationale for how MFE thresholds are derived, however, is not obvious when examining the literature. In fact, the guidelines proposed for uniform determination and annotation of miRNAs given by Ambros et al. do not mention MFE thresholds. Instead, the guidelines merely suggest that to be considered a miRNA, a candidate's lowest MFE fold should be a hairpin .
Determination of MFE threshold is dependent on tolerance to false positives, i.e. higher MFE thresholds result in inclusion of more candidates. A threshold of -20 kcal/mol has been generally used, but levels as high as -12 kcal/mol have also been explored . We used a middle value of -15 kcal/mol as this genome has not been previously explored.
From the 110 unique S. mansoni sequences returned from the BLASTn search, 66 displayed MFE values of -15 kcal/mol or less when folded with RNAshapes. Forty three hairpins had MFE values greater than -15 kcal/mol.
All 66 of the extended sequences with MFE ≤ -15 kcal/mol displayed hairpins in at least one portion of the sequence when folded by RNAshapes. At ~122nt in length, the sequence is considerably longer than a typical miRNA hairpin, and as a result, only the ~70nt surrounding the mature sequence are of interest. This region was considered the candidate pre-miRNA sequence. In each of the 66 hairpins detected, the region surrounding the mature sequence was within a hairpin.
Of the 66 hairpins with MFE ≤ -15 kcal/mol, 36 structures contained the mature sequence entirely within the stem. The other 30 sequences contained the mature sequence partially or completely in a loop.
The yield of probable miRNA candidates was much lower for this analysis with S. mansoni than analyses of species that contain closer relatives in miRBase. The findings suggest that it may have been difficult to find a large number of miRNAs in this analysis, due to the possibility of a large amount of sequence divergence between S. mansoni and its closest relative in miRBase is S. mediterranea. On the contrary, if one was interested in studying miRNAs in a mammal not found in miRBase, one would find 22 members from the same class and over 2,500 miRNA sequences in miRBase. However, for now, organisms such as S. mansoni must continue to have a mix of computational and experimental approaches, with an emphasis on experimental discovery.
Comparison of results to existing work
The high-throughput pipeline yielded fifteen probable pre-miRNA candidates. This number is comparable to the number of miRNAs found by Palokodeti et al., who identified ten miRNA candidates in S. mediterranea by using all known human, Drosophila and C. elegans miRNAs as reference sequences . However, the yields for the S. mediterranea analysis and for this analysis with S. mansoni are considerably smaller than other studies that have used homology methods with multiple genomes. Luo et al. identified 118 miRNAs in Tribolium castaneum (red flour beetle) using all available metazoan miRNAs as reference sequences . Zhou et al. using a homology-based computational approach, found 300 human miRNA homologs in the domestic dog using only human miRNAs as the reference miRNAs . Furthermore, Baev et al. identified 639 chimpanzee miRNAs with a homology-based approach, also using only human pre-miRNA sequences as a reference set . Recently, novel miRNAs were identified in Schistosoma japonicum, a close relative of S. mansoni[37, 48–50]. Chatterjee and Chaudhuri detected 489 homologous miRNA sequences in the Anopheles gambiae genome, using only Drosophila miRNA as the query sequence . Drosophila is a well represented group of organisms in miRBase, but it is also in the same order as A. gambiae. As a result, this close relationship produced a large number of hits. Vertebrates, especially mammals, are currently the most represented organisms in miRBase, with respect to number of species and number of miRNA sequences.
It is worthwhile to note the extent to which the yield decreases as the distance between species grows. As a result, these findings suggest that the yield of a homology-based analysis is very dependent on the available content of miRBase. Artzi et al. used their recently released homology search web-server, miRNAminer, to increase the number of miRBase miRNAs for seven mammals by 50%, identifying 790 new miRNAs . The strategy and filtering steps used by miRNAminer are very similar to those used in this paper, but moderately more comprehensive.
Analysis of homology search hits by species
Observed miRNA families
The discovery of small regulatory RNA molecules, miRNAs, is undoubtedly one of the most important recent findings in biological research. This study demonstrates for the first time the presence of miRNAs in S. mansoni identified by complementary experimental and computational approaches. By cloning and sequencing of 1200 sequences from a small RNA library, 211 potential miRNA candidates were identified, of which 26 were predicted to form stem-loop structures characteristic of miRNA precursors. The expression of 14 of them was confirmed by northern blot analysis. The homology search by the high-throughput pipeline was performed with all known miRNAs in miRBase and fifteen novel likely miRNAs were detected in the parasitic organism S. mansoni. The identification of miRNA in the S. mansoni genome presents relevant information that is likely to be important to study various aspects as parasite development, gene regulation, evolutionary processes and sexual maturation.
Parasites and nucleic acid extraction
Total RNA was extracted from adult worm pairs and lung-stage schistosomula of S. mansoni with use of Trizol® (Invitrogen). Cercariae were obtained from infected Biomphalaria glabrata snails and isolated parasite bodies were prepared as previously described . Schistosomula were cultured for 7 days in complete RPMI medium supplemented with 10 mM Hepes, 2 mM glutamate, 5% fetal calf serum and antibiotics (100 U/ml penicillin and 100 μg/ml streptomycin) at 37°C in a 5% CO2 atmosphere.
RNA isolation and miRNA cloning
A total of 5 aliquots with 200 μg of total RNA isolated from adult worms by guanidine thiocyanate phenol-chloroform extraction were pooled . The short RNA fraction ranging from 17 to 26 nt was purified and cloned as described in Chappell et al.. Briefly, the concentration was quantified using the NanoDrop Spectrophotometer (NanoDrop Technologies, USA). Total RNA (1 mg) was resolved by electrophoresis on 15% denaturing polyacrylamide gel (8 M urea, 1 × TBE buffer), and short RNAs (17 to 26 nucleotides in length) were excised and eluted in 3 M NaCl solution at 4°C for 16 h. The gel purified small RNAs were dephosphorylated using APex™ Heat-Labile Alkaline Phosphatase (Epicentre) and ligated directly to a 5'-phosphorylated 3'-adapter oligonucleotide with a blocked 3'-hydroxyl terminus (5'-pUUUaaccgcatccttctcx-3'; uppercase, RNA; lowercase, DNA; p, phosphate; x, inverted deoxythymidine) (Dharmacon Research, Boulder, CO) to prevent self-ligation. The ligation products were separated from the excess of 3'-adapter on a 15% denaturing polyacrylamide gel and were subsequently ligated to a non-phosphorylated 5'-adater oligonucleotide (5'-tactaatacgactcactAAA-3'; uppercase, RNA; lowercase, DNA) (Dharmacon Research, Boulder, CO) using T4 RNA ligase (Invitrogen). The final products were again gel purified by size fractionation and submitted to reverse transcription reaction using the RT primer (5'-TTTTCTGCAG AAGGATGCGGTTAAA-3'; bold, Pst I site). This was followed by high fidelity PCR amplification using the reverse (RT primer) and forward (5'-AAACCATGG TACTAATACGACTCACTAAA-3'; bold, Nco I site). The PCR products were digested with Pst I and Nco I and subsequently concatenated using T4 DNA ligase. The ends of the concatamers were filled in with Klenow/AT-tailing and ligated into a 2.1 TOPO TA vector (Invitrogen). Ligated plasmids were transformed into TOP10 cells (Invitrogen). The libraries were plated on Luria-Bertani (LB) ampicillin (100 μg/ml) plates and individual colonies were picked and put into 96-well plates containing LB ampicillin and grown overnight at 37°C with continuous shaking. The recombinant clones were selected, sequenced and the data was analyzed as described below.
Computational analysis of microRNA library sequences
Base calling and quality trimming of sequence chromatograms were conducted using PHRED . After masking of vector and adapter sequences using EMBOSS-restrict http://bioweb2.pasteur.fr/docs/EMBOSS/restrict.html, small RNA sequences ranging 17-25 nt in length were aligned by ClustalW2 program and redundant sequences removed .
The unique sequences were used in BLAST searches against the S. mansoni genome and miRBase database (http://microrna.sanger.ac.uk; release 13.0) to identify sequences from other species that closely match candidate S. mansoni miRNAs and removal of contaminating mRNAs, tRNAs, rRNAs, and other small RNAs. To predict the secondary structure of the remaining small RNA, Perl scripts were implemented to align the sequences to the genome of the parasite S. mansoni (http://www.schistodb.net Genome version 4.0) aiming at retrieving all possible genomic locations. In brief, the script executes BLAST to perform sequence similarity analysis and the result is parsed to retrieve the genomic positions to which each miRNA aligns . In the next step, the script builds a FASTA file containing two sets of approximately 500 entries for each miRNA: one set of genomic sequences plus 40, 50, 60 or 70 nucleotides upstream and downstream. The secondary structures were predicted for each sequence using RNAfold from the Vienna RNA package . Each image was further visually inspected to confirm the presence of a typical stem-loop conformation of pre-miRNAs. Among all structures created for each sequence, the one containing the mature miRNA in one arm of the hairpin precursor and with lowest folding free energy was selected. The final images were created using the VARNA http://varna.lri.fr/index.html to insert subtitles and highlight the mature miRNA sequence.
miRNA expression analysis
For the northern blot analysis, total RNA from adult worm pairs and 7-day in vitro cultured schistosomula were used. Sixty micrograms of total RNA were separated on 15% denaturing polyacrylamide gels and electrotransferred to Hybond N+ membranes (GE Healthcare) in 1x Tris Borate EDTA using the Mini Trans-Blot Cell apparatus (Bio-Rad), according to the manufacturer's instructions. Membranes were UV cross-linked in the UV Stratalinker® (Stratagene) and pre-hybridized in DIG Easy Hyb solution (Roche) at 37°C for 30 min. DNA oligonucleotides complementary to the miRNA sequences were labeled with DIG Oligo 3'-End Labeling Kit, Second generation (Roche). Hybridization was performed overnight at 37°C with 3' digoxigenin-labeled RNA probes at 4.5 pmol/μl. The membranes were washed using the DIG Wash (Roche) and blocked with Block Buffer Set (Roche). In brief, blots were incubated in blocking solution for 1 hour and then in antibody solution (anti-DIG, alkaline phosphatase conjugated antibody, 250 mU/ml) for 30 min, followed by washing twice in washing buffer. After equilibration in detection buffer, blots were incubated with chemiluminescent substrate CSPD (Roche). Membranes were exposed to X-ray film for 20 minutes and the films were digitized using a transmission scanner GS-800 Calibrated Densitometer (Bio-Rad).
Computational identification of additional miRNAs
The first step was a BLASTn search, performed with all mature miRNA sequences downloaded from miRBase (release 13.0) against the S. mansoni genome (version 4.0). The expectation value cutoff for the pipeline development was set at 0.01. Similarly to the analysis of the microRNA libraries, the candidate miRNA sequences from the S. mansoni genome, plus 50nt on each side of the candidate mature miRNA sequence were selected using the MATLAB Bioinformatics toolbox. These extended sequences were then used for further analysis with the understanding that the sequence contained the candidate mature sequence, candidate hairpin, and extra nucleotides. Extended candidate miRNA sequences were folded using the standalone version of RNAshapes, which generates multiple folds for each sequence, ranking them by MFE. Each image was further visually inspected to confirm the presence of a typical stem-loop conformation of pre-miRNAs. Among all structures created for each sequence, the one containing the mature miRNA in one arm of the hairpin precursor and with lowest folding free energy was selected.
Visual inspection of miRNA secondary structures
During method development, a mix of rules-based filters (e.g. MFE) and manual/visual inspection of the folded extended miRNA were used to determine probable pre-miRNA candidates. In the development of the pipeline, with an emphasis on automation, the following rules-based filters were developed and implemented:
Folded extended miRNA sequences with MFE greater than -15 kcal/mol were removed.
Structures with the mature or partial miRNA contained in the loop of the hairpin were excluded, i.e. no bases in the mature miRNA can be represented by dots that are between opposing parentheses. For example, the structure in Figure 12B was filtered out of the analysis. The mature miRNA sequence is underlined in the sequence and in the dot-bracket diagram. If only one major hairpin is present, the candidate pre-miRNA sequence is found within the bases from 1) the third base from the end of the mature miRNA sequence away from the loop to 2) the last base on the hairpin end of the extended miRNA sequence. A major hairpin was defined as one that extends >75% of the extended miRNA sequence. The selected candidate pre-miRNA sequence is shown underlined in Figure 12C.
When selecting the candidate pre-miRNA, if additional paired bases were directly adjacent to the selected sequence, the selection was extended to include these bases. This step prevents known stems of the hairpin from being truncated. The two additional bases, 'UC' (shown italicized), on the left of the selected sequence in Figure 12D illustrates this rule.
If either end of the selected hairpin sequence terminates in paired bases, while the other end of the sequence terminates in unpaired bases, the paired end of the sequence was extended by the number of unpaired bases on the other end. Extending the sequence required extracting the additional bases from the S. mansoni database. The rationale for this rule was that unpaired bases on the miRNA end may not actually be unpaired, but instead the bases that they pair with were merely not present in the original extended sequence. After the additional bases were added, the sequence was refolded. If the newly added bases were unpaired, the original fold was used. In the example in Figure 12E, the six bases on the left of the selected sequence, 'GAUCGA', were unpaired. However, the other end of the selected sequence ended in paired bases. As a result, six additional bases were added and the sequence was refolded as shown in Figure 12F.
In cases where two or more hairpins were present in the extended sequence, two sequence selections are made, i.e. on either side of the mature sequence. The rules described above were then followed as shown in Figure 12G.
Unpaired bases at the ends of the hairpin stems that are not part of the mature miRNA sequence or the 3nt extension were removed.
The candidate pre-miRNA sequences were folded using RNAshapes. Structures with MFE ≤ -15 kcal/mol were considered probable pre-miRNA sequences.
This work was funded by NIH-NIAID Grant 5D43TW007012-04, FAPEMIG (CBB-1181/08 and 5323-4.01/07), Capes, CEBio, CNPq - INCT (573839/2008-5) and Fiocruz (GO), NIH Training Grant D43TW006580 (PLV), NIH Grant U01-AI48828 (NMES). GO is a CNPq fellow (306879/2009-3). The parasites were kindly provided by Fred Lewis (Biomedical Research Institute through NIH-NIAID, MD) and Laboratory of Mollusks, Centro de Pesquisas René Rachou-Fiocruz, Belo Horizonte, Brazil.
- Kim VN, Nam JW: Genomics of microRNA. Trends Genet. 2006, 22: 165-173. 10.1016/j.tig.2006.01.003.PubMedView ArticleGoogle Scholar
- Huttenhofer A, Vogel J: Experimental approaches to identify non-coding RNAs. Nucleic Acids Res. 2006, 34: 635-646. 10.1093/nar/gkj469.PubMed CentralPubMedView ArticleGoogle Scholar
- He L, Hannon GJ: MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004, 5: 522-31. 10.1038/nrg1379.PubMedView ArticleGoogle Scholar
- Valencia-Sanchez MA, Liu J, Hannon GJ, Parker R: Control of translation and mRNA degradation by miRNAs and siRNAs. Genes Dev. 2006, 20: 515-524. 10.1101/gad.1399806.PubMedView ArticleGoogle Scholar
- Lagos-Quintana M, Rauhut R, Meyer J, Borkhardt A, Tuschl T: New microRNAs from mouse and human. RNA. 2003, 9: 175-179. 10.1261/rna.2146903.PubMed CentralPubMedView ArticleGoogle Scholar
- Wiznerowicz M, Szulc J, Trono D: Tuning silence: conditional systems for RNA interference. Nature Methods. 2006, 3: 682-688. 10.1038/nmeth914.PubMedView ArticleGoogle Scholar
- Lau NC, Lai EC: Diverse roles for RNA in gene regulation. Genome Biol. 2005, 6: 315-10.1186/gb-2005-6-4-315.PubMed CentralPubMedView ArticleGoogle Scholar
- Ambros V: The functions of animal's microRNAs. Nature. 2004, 431: 350-355. 10.1038/nature02871.PubMedView ArticleGoogle Scholar
- Miska A: How microRNAs control cell division, differentiation and death. Curr. Opin. Genet. Dev. 2005, 15: 563-568. 10.1016/j.gde.2005.08.005.PubMedView ArticleGoogle Scholar
- Bushati N, Cohen SM: microRNA functions. Annual Review of Cell and Developmental Biology. 2007, 23 (I): 175-205. 10.1146/annurev.cellbio.23.090506.123406.PubMedView ArticleGoogle Scholar
- Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, Radmark O, Kim S, Kim VN: The nuclear RNase III Drosha initiates microRNA processing. Nature. 2003, 425: 415-419. 10.1038/nature01957.PubMedView 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: 228-34. 10.1038/ncb0309-228.PubMedView ArticleGoogle Scholar
- Lund E, Guttinger S, Calado A, Dálberg JE, Kutay U: Nuclear export of microRNA precursors. Science. 2003, 303: 95-98. 10.1126/science.1090599.PubMedView ArticleGoogle Scholar
- Lewis BP, Shi I, Jones-Rhoades MW, Bartel DP, Burge CB: Prediction of mammalian microRNA targets. Cell. 2003, 115: 787-798. 10.1016/S0092-8674(03)01018-3.PubMedView ArticleGoogle Scholar
- Lee RC, Ambros V: An extensive class of small RNAs in Caenorhabditis elegans. Science. 2001, 294: 862-864. 10.1126/science.1065329.PubMedView ArticleGoogle Scholar
- Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T: Identification of tissue-specific microRNAs from mouse. Curr Biol. 2002, 12: 735-739. 10.1016/S0960-9822(02)00809-6.PubMedView ArticleGoogle Scholar
- Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall M, Matzke M, Ruvkun G, Tuschl T: A uniform system for microRNA annotation. RNA. 2003, 9: 277-279. 10.1261/rna.2183803.PubMed CentralPubMedView ArticleGoogle Scholar
- Kloosterman WP, Steiner FA, Berezikov E, de Bruijn E, van de Belt J, Verheul M, Cuppen E, Plasterk RH: Cloning and expression of new microRNAs from zebrafish. Nucleic Acids Res. 2006, 34: 2558-2569. 10.1093/nar/gkl278.PubMed CentralPubMedView ArticleGoogle Scholar
- Lai EC, Tomancak P, Williams RW, Rubin GM: Computational identification of Drosophila microRNA genes. Genome Biol. 2003, 4: R42-10.1186/gb-2003-4-7-r42.PubMed CentralPubMedView ArticleGoogle Scholar
- Lim LP, Lau NC, Weinstein EG, Abdelhakim A, Yekta S, Rhoades MW, Burge CB, Bartel DP: The microRNAs of Caenorhabditis elegans. Genes Dev. 2003, 17: 991-1008. 10.1101/gad.1074403.PubMed CentralPubMedView ArticleGoogle Scholar
- Lim LP, Glasner ME, Yekta S, Burge CB, Bartel DP: Vertebrate microRNAs genes. Science. 2003, 299: 1540-10.1126/science.1080372.PubMedView ArticleGoogle Scholar
- Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993, 75: 843-854. 10.1016/0092-8674(93)90529-Y.PubMedView ArticleGoogle Scholar
- Hertel J, Lindemeyer M, Missal K, Fried C, Tanzer A, Flamm C, Hofacker IL, Stadler PF, Students of Bioinformatics Computer Labs 2004 and 2005: The expansion of the metazoan microRNA repertoire. BMC genomics. 2006, 7: 25-10.1186/1471-2164-7-25.PubMed CentralPubMedView ArticleGoogle Scholar
- Savioli L, Stansfield S, Bundy DA, Mitchell A, Bhatia R, Engels D, Montresor A, Neira M, Shein AM: Schistosomiasis and soil-transmitted helminth infections: forging control efforts. Trans R Soc Trop Med Hyg. 2002, 96: 577-579. 10.1016/S0035-9203(02)90316-0.PubMedView ArticleGoogle Scholar
- Bumcrot D, Manoharan M, Koteliansky V, Sah DW: RNAi therapeutics: a potential new class of pharmaceutical drugs. Nat Chem Biol. 2006, 2: 711-719. 10.1038/nchembio839.PubMedView ArticleGoogle Scholar
- Cheng G, Fu Z, Lin J, Shi Y, Zhou Y, Jin Y, Cai Y: In vitro and in vivo evaluation of small interference RNA-mediated gynaecophoral canal protein silencing in Schistosoma japonicum. J Gene Med. 2009, 11: 412-421. 10.1002/jgm.1314.PubMedView ArticleGoogle Scholar
- Krautz-Peterson G, Simoes M, Faghiri Z, Ndegwa D, Oliveira G, Shoemaker CB, Skelly PJ: Suppressing glucose transporter gene expression in schistosomes impairs parasite feeding and decreases survival in the mammalian host. PLoS Pathog. 2010, 6: e1000932-10.1371/journal.ppat.1000932.PubMed CentralPubMedView ArticleGoogle Scholar
- Chappell L, Baulcombe D, Molnár A: Isolation and cloning of small RNAs from virus-infected plants. Current Protocols in Microbiology. 2006, Chapter 16:Unit 16H.2Google Scholar
- Pearson WR: Flexible sequence similarity searching with the FASTA3 program package. Methods Mol. Biol. 2000, 132: 185-219.PubMedGoogle Scholar
- Carthew RW: Molecular biology. A new RNA dimension to genome control. Science. 2006, 313: 305-306. 10.1126/science.1131186.PubMedView ArticleGoogle Scholar
- Berriman M, Haas BJ, LoVerde PT, Wilson RA, Dillon GP, Cerqueira GC, Mashiyama ST, Al-Lazikani B, Andrade LF, Ashton PD, et al: The genome of the blood fluke Schistosoma mansoni. Nature. 2009, 460: 352-358. 10.1038/nature08160.PubMed CentralPubMedView ArticleGoogle Scholar
- Chaudhuri K, Chatterjee R: MicroRNA detection and target prediction: integration of computational and experimental approaches. DNA Cell Biol. 2007, 26: 321-337. 10.1089/dna.2006.0549.PubMedView ArticleGoogle Scholar
- Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, et al: Amammalian microRNA expression atlas based on small RNA library sequencing. Cell. 2007, 129: 1401-1414. 10.1016/j.cell.2007.04.040.PubMed CentralPubMedView ArticleGoogle Scholar
- Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, Carrington JC: 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 CentralPubMedView ArticleGoogle Scholar
- Krautz-Peterson G, Skelly PJ: Schistosoma mansoni: the dicer gene and its expression. Exp Parasitol. 2008, 118: 122-128. 10.1016/j.exppara.2007.06.013.PubMed CentralPubMedView ArticleGoogle Scholar
- Krautz-Peterson G, Radwanska M, Ndegwa D, Shoemaker CB, Skelly PJ: Optimizing gene suppression in schistosomes using RNA interference. Mol Biochem Parasitol. 2007, 153: 194-202. 10.1016/j.molbiopara.2007.03.006.PubMedView ArticleGoogle Scholar
- Xue X, Sun J, Zhang Q, Wang Z, Huang Y, Pan W: Identification and characterization of novel microRNAs from Schistosoma japonicum. Plos One. 2008, 3: e4034-10.1371/journal.pone.0004034.PubMed CentralPubMedView ArticleGoogle Scholar
- Ding X, Weiller J, Großhans H: Regulating the regulators: mechanisms controlling the maturation of microRNAs. Trends in Biotech. 2009, 27: 27-36. 10.1016/j.tibtech.2008.09.006.View ArticleGoogle Scholar
- Grishok A, Pasquinelli AE, Conte D, Li N, Parrish S, Ha I, Baillie DL, Fire A, Ruvkun G, Mello CC: Genes and mechanisms related to RNA interference regulate expression of the small temporal RNAs that control C. elegans developmental timing. Cell. 2001, 106: 23-34. 10.1016/S0092-8674(01)00431-7.PubMedView ArticleGoogle Scholar
- Ro S, Song R, Park C, Zheng H, Sanders KM, Yan W: Cloning and expression profiling of small RNAs expressed in the mouse ovary. RNA. 2007, 13: 2366-2380. 10.1261/rna.754207.PubMed CentralPubMedView ArticleGoogle Scholar
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116: 281-297. 10.1016/S0092-8674(04)00045-5.PubMedView ArticleGoogle Scholar
- Steffen B, Voß B, Rehmsmeier M, Reeder J, Giegerich R: RNAshapes: an integrated RNA analysis package based on abstract shapes. Bioinformatics. 2006, 22: 500-503. 10.1093/bioinformatics/btk010.PubMedView ArticleGoogle Scholar
- Hsu P, Huang H, Hsu S, Lin L, Tsou A, Tseng C, Stadler P, Washietl S, Hofacker : miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes. Nucleic Acids Research. 2006, 34: 135-139. 10.1093/nar/gkj135.View ArticleGoogle Scholar
- Palakodeti D, Smielewski M, Graveley B: MicroRNAs from the planarian Schmidtea mediterranea: a model system for cell biology. RNA. 2006, 12: 1640-1649. 10.1261/rna.117206.PubMed CentralPubMedView ArticleGoogle Scholar
- Luo Q, Zhou Q, Yu X, Lin H, Hu S, Yu J: Genome-wide mapping of conserved microRNAs and their host transcripts in Tribolium castaneum. J. Genetics and Genomics. 2008, 35: 349-355. 10.1016/S1673-8527(08)60051-X.View ArticleGoogle Scholar
- Zhou D, Li S, Wen J, Gong X, Xu L, Luo Y: Genome wide computational analyses of microRNAs and their targets from Canis familiaris. Comput. Biol. Chem. 2008, 32: 61-66. 10.1016/j.compbiolchem.2007.08.007.View ArticleGoogle Scholar
- Baev V, Daskalova E, Minkov : Computational identification of novel microRNA homologs in the chimpanzee genome. Comput. Biol. Chem. 2009, 33: 62-70. 10.1016/j.compbiolchem.2008.07.024.PubMedView ArticleGoogle Scholar
- Hao L, Cai P, Jiang N, Wang H, Chen Q: Identification and characterization of microRNAs and endogenous siRNAs in Schistosoma japonicum. BMC Genomics. 2010, 11: 55-10.1186/1471-2164-11-55.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang Z, Xue X, Sun J, Luo R, Xu X, Jiang Y, Zhang Q, Pan W: An "in-depth" description of the small non-coding RNA population of Schistosoma japonicum schistosomulum. PLoS Negl Trop Dis. 2010, 4: e596-10.1371/journal.pntd.0000596.PubMed CentralPubMedView ArticleGoogle Scholar
- Huang J, Hao P, Chen H, Hu W, Yan Q, Liu F, Han ZG: Genome-wide identification of Schistosoma japonicum microRNAs using a deep-sequencing approach. PLoS One. 2009, 4: e8206-10.1371/journal.pone.0008206.PubMed CentralPubMedView ArticleGoogle Scholar
- Chatterjee R, Chaudhuri K: An approach for the identification of microRNA with an application to Anopheles gambiae. Acta Biochimica Polonica. 2006, 53: 303-309.PubMedGoogle Scholar
- Artzi S, Kiezun A, Shomron N: miRNAminer: A tool for homologous microRNA gene search. BMC Bioinformatics. 2008, 9: 39-10.1186/1471-2105-9-39.PubMed CentralPubMedView ArticleGoogle Scholar
- Skelly PJ, Da'dara A, Harn DA: Suppression of cathepsin B expression in Schistosoma mansoni by RNA interference. Int J Parasitol. 2003, 33: 363-369. 10.1016/S0020-7519(03)00030-4.PubMedView ArticleGoogle Scholar
- Chomczynski P, Sacchi N: Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem. 162: 156-159. 10.1016/0003-2697(87)90021-2.
- Ewing B, Hillier L, Wendl MC, Green P: Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 1998, 8: 175-185.PubMedView ArticleGoogle Scholar
- Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22: 4673-4680. 10.1093/nar/22.22.4673.PubMed CentralPubMedView ArticleGoogle Scholar
- Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ, et al: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralPubMedView ArticleGoogle Scholar
- Hofacker IL: Vienna RNA secondary structure server. Nuc. Acids Res. 2003, 31: 3429-3431. 10.1093/nar/gkg599.View ArticleGoogle Scholar
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