Characterisation of novel microRNAs in the Black flying fox (Pteropus alecto) by deep sequencing
© Cowled et al.; licensee BioMed Central Ltd. 2014
Received: 18 December 2013
Accepted: 7 August 2014
Published: 15 August 2014
Bats are a major source of new and emerging viral diseases. Despite the fact that bats carry and shed highly pathogenic viruses including Ebola, Nipah and SARS, they rarely display clinical symptoms of infection. Host factors influencing viral replication are poorly understood in bats and are likely to include both pre- and post-transcriptional regulatory mechanisms. MicroRNAs are a major mechanism of post-transcriptional gene regulation, however very little is known about them in bats.
This study describes 399 microRNAs identified by deep sequencing of small RNA isolated from tissues of the Black flying fox, Pteropus alecto, a confirmed natural reservoir of the human pathogens Hendra virus and Australian bat lyssavirus. Of the microRNAs identified, more than 100 are unique amongst vertebrates, including a subset containing mutations in critical seed regions. Clusters of rapidly-evolving microRNAs were identified, as well as microRNAs predicted to target genes involved in antiviral immunity, the DNA damage response, apoptosis and autophagy. Closer inspection of the predicted targets for several highly supported novel miRNA candidates suggests putative roles in host-virus interaction.
MicroRNAs are likely to play major roles in regulating virus-host interaction in bats, via dampening of inflammatory responses (limiting the effects of immunopathology), and directly limiting the extent of viral replication, either through restricting the availability of essential factors or by controlling apoptosis. Characterisation of the bat microRNA repertoire is an essential step towards understanding transcriptional regulation during viral infection, and will assist in the identification of mechanisms that enable bats to act as natural virus reservoirs. This in turn will facilitate the development of antiviral strategies for use in humans and other species.
KeywordsBats Chiroptera Pteropus alecto MicroRNA Non-coding RNA Transcriptome
More than 20% of all mammalian species are bats, making them an extraordinarily important and successful group from an evolutionary perspective . Bats are unique amongst mammals for their ability to fly, and possess notable traits such as long life expectancy in proportion to body size . Many species of bats exhibit exotic traits including echolocation and hibernation, and bats are an important part of the ecosystem via plant pollination and insect control . Notoriously, bats are also reservoir hosts for a large number of zoonotic viruses . Understanding the mechanisms by which bats co-exist with and seemingly tolerate viruses that are deadly in humans and other mammals has implications for human health, and may facilitate development of new antiviral strategies.
One aspect of the bat-virus relationship that has not been investigated in detail is the role of host gene regulation, in particular the role of microRNAs (miRNAs). MiRNAs are essential regulators of eukaryotic gene expression  and include elements required for viral replication . MiRNA biogenesis is a multistep process in which primary-miRNA transcripts (pri-miRNA) are cleaved into precursor-miRNA (pre-miRNA) by the nuclear RNase III DROSHA and then further processed in the cytoplasm by DICER1 to produce mature miRNAs averaging 22 nt in length . This process produces transcripts from both arms of the precursor, whereas the loop and flanking sequences are destroyed. This information is exploited by miRNA-finding algorithms such as miRDeep2 for the identification of miRNA-like sequences in small RNA transcriptome data.
Mature miRNAs have been found in over 150 species of plants, animals and viruses [7, 8]. While no P. alecto miRNAs have yet been reported, recent studies identified miRNAs in the Little brown bat Myotis lucifugus[9–12], the Big brown bat, Eptesicus fuscus, and the Jamaican flying fox, Artebius jamaicensis. Additionally, bat genomes within the Ensembl database (P. vampyrus and M. lucifugus) feature miRNA annotations based on homology to the human genome.
In this study, the small RNA transcriptome of the Black flying fox (P. alecto) was sequenced from a pooled tissue sample. MiRDeep2 was used to identify conserved and novel miRNAs, and a variety of methods were employed to assess those predictions. Target prediction and annotation enrichment analysis were undertaken to identify miRNAs with putative roles in pathways that are relevant to virus-host interaction. These results will enable fundamental insights into miRNA-mediated gene regulation in bats, and are an important step towards determining the molecular basis of bats’ role as virus reservoirs.
Identification of P. alectomiRNAs
MiRDeep2 flagged four putative miRNAs as possible tRNA/rRNA. To incorporate the latest knowledge of non-coding (nc) RNA, we compared predicted P. alecto miRNA precursors with all known ncRNA in a recent version of RFAM (version 11.0) (Additional file 4: Table S3). A total of 222 P. alecto miRNAs were supported by RFAM hits to known miRNAs. A further nineteen exhibited similarity to other types of ncRNA. Hits to ncRNAs other than miRNAs do not necessarily preclude candidates from being true miRNAs, however they may be less reliable than other candidates.
Homology to miRNAs in other species
Notably, two P. alecto precursors (pal-can-280 and pal-can-392) had large numbers of hits to P. vampyrus precursors (38 and 40 hits, respectively), in each case representing a variety of similar but different sequences rather than one single repeated sequence. In contrast, a single P. alecto precursor (pal-can-136) had a similarly high number of hits in M. lucifugus, however this appeared to be due to low-complexity composition of the precursor (GU repeats) as opposed to genuine homology.
MiRNAs in introns and clusters
Many miRNAs are located within the introns of protein-coding genes . We identified 98 miRNAs physically located within introns of annotated bat genes, while three (pal-can-011, pal-can-306, pal-can-346) were fully overlapping with coding exons (Additional file 6: Table S5). Of these, pal-can-346 (miR-1306) is located within an exon of DGCR8, a protein involved in miRNA biogenesis in humans . Five miRNAs overlapped with exon boundaries and could potentially be regulated by splicing events .
Many miRNAs are arranged in local clusters on chromosomes . The P. alecto genome currently consists of contigs and scaffolds that are not yet mapped to individual chromosomes, however with an N50 size of 15.8 Mb , many scaffolds are easily large enough to contain such clusters. MiRNA clusters were located by identifying miRNAs spaced less than 5000 bp apart. miRNA pairs located < 60 bp apart were shown to be overlapping as described above. Such pairs were treated as a single entity and were only considered part of a cluster if they grouped with at least one other non-overlapping miRNA. A total of 42 clusters comprising 140 miRNA genes were identified according to these criteria (Additional file 7: Table S6).
The largest identified bat miRNA cluster contained 33 miRNAs and is homologous to the recognised DLK1-DIO3 cluster on human chromosome 14, known to be involved in disease pathogenesis . Three novel bat miRNAs were located within this cluster, including one (pal-can-276) that returned BLAST hits to miR-541 but had three mismatches relative to other vertebrates, including two unique differences in the seed region. For the two other novel bat miRNAs within this cluster (pal-can-411 and pal-can-252), the mature sequences produced non-identical BLAST hits to miR-376 and miR-377, respectively, however the star sequences were 100% identical to miR-376 and miR-377 star sequences, respectively.
Seed sequence analysis
The nucleotide sequence corresponding to bases 2–7 of a mature miRNA (the seed region) is the most important region for determining target specificity . The seed sequences of P. alecto miRNAs were assessed (Additional file 9: Table S7). The 399 bat miRNAs had 273 different seed sequences with 63 seed sequences appearing more than once. The most frequently occurring seeds were GAGGUA (let-7), which appeared 12 times and CUGGAC (mir-378), which appeared 9 times. Compared to miRBase vertebrate mature miRNAs, 40 bat miRNAs (two pairs plus 17 singletons) appeared to have novel seed sequences. A potential weakness of this approach, however, is that if a miRNA has an incorrectly predicted start site, then the predicted seed sequence will also be incorrect. Therefore, only miRNAs that aligned from position #1 in both the bat mature sequence and its top BLAST hit were considered reliable enough to determine true seed sequence. Of the 49 miRNAs with non-identical top BLAST hits, 30 aligned from position #1, however only one of these (pal-can-316) had a seed sequence that was unique amongst miRBase vertebrates.
Three of the five novel-seed miRNAs (pal-can-276, pal-can-133 and pal-can-316) had both mature and star sequences that were unique to P. alecto in the sense that they were not shared by any of the BLAST hits to these sequences. Two of these (pal-can-276 and pal-can-133) had seed sequences (AGGGGU and GCCUAG) that were shared by single vertebrate entries in miRBase (gga-miR-1761 and hsa-miR-3135a, respectively), yet they did not resemble those miRNAs in any other respect. In contrast, pal-can-316 seed sequence (UCGCAA) was completely unique amongst vertebrates, in the sense that it was not shared by any vertebrate miRNA, including unrelated sequences. These three miRNAs had mature/star read counts of 162/0, 36/8 and 10/0, respectively.
The two remaining P. alecto miRNAs (pal-can-411 and pal-can-134) had mature seed sequences distinct from those of their BLAST hits, however their star sequences matched with 100% identity to miRBase entries. Of these, pal-can-134 had mature/star read counts of 31/1; however only a single mature read was detected for pal-can-411.
Ranking of novel candidate miRNAs
Top 30 novel miRNAs in P. alecto
Top BLAST hit
It has been recognised that 3’ UTRs are the preferred location for miRNA binding sites in mRNA targets . Target prediction was performed for the 399 mature P. alecto miRNAs using the target scanning algorithm miRanda and a database of 3’ UTRs derived from P. alecto transcriptome data. To avoid potential artefacts, excessively long 3’ UTRs (those more than 2500 nt in length) were excluded, resulting in a search space representing 6021 different P. alecto genes corresponding to 5899 different human genes (the remaining 122 are bat-specific gene duplications). The median number of predicted gene targets per miRNA was 77, while the median number of predicted miRNA hits per target gene was 7, which in some cases included multiple hits from an individual miRNA.
The highest-scoring miR:Target prediction was pal-can-170:USP36, which had 10 binding sites and a cumulative minimum free energy (E_sum) of −217.80. Other high scoring miR:Target predictions were pal-can-421:DNMBP (six binding sites, E_sum = −152.27); and pal-can-207/pal-can-098:ZNF768 (six binding sites each, E_sum = −143.30), and pal-can-290:RAPGEF1 (six binding sites, E_sum = −138.79). The highest-scoring miR:Target predictions for individual sites were pal-can-194:ALG3 with a minimum free energy (E) of −50.51; and pal-can-300:FANCI (E = −45.27).
Selected gene target predictions for novel P. alecto miRNA pal-can-276 (miR-541)
Pteropus alecto gene
Minimum free energy (Best hit)
3’ UTR length
ubiquitin specific peptidase 20
DNA-damage-inducible transcript 4
CXXC finger 5
ubiquitin specific peptidase 19
polymerase (DNA directed), mu
tumor necrosis factor receptor superfamily, member 1B
leucine rich repeat containing 32
leucine rich repeat (in FLII) interacting protein 2
cytokine receptor-like factor 1
interleukin 28 receptor, alpha (interferon, lambda receptor)
ATG9 autophagy related 9 homolog A (S. cerevisiae)
apoptosis-inducing factor, mitochondrion-associated, 2
zinc finger and AT hook domain containing
Categorical enrichment analysis based on Gene Ontology (GO) and KEGG pathway annotations were performed using the lists of predicted target genes for each miRNA (Additional file 11: Table S9, Additional file 12: Table S10). Ranked by Bonferroni-corrected p-values, the most significantly enriched GO terms were GO:0006357: “regulation of transcription from RNA polymerase II promoter” (pal-can-247) and GO:0000278: “mitotic cell cycle” (pal-can-379); GO:0006325: “chromatin organization” (pal-can-354). The most significantly enriched KEGG pathways were hsa04912: GnRH signalling pathway (pal-can-085); hsa04060: Cytokine-cytokine receptor interaction (pal-can-088); hsa04012: ErbB signalling pathway (pal-can-170); and hsa03030: DNA replication (pal-can-300).
Bats have long held special significance in the contexts of ecology and mammalian evolution, but have only recently been recognised as a major source of emerging infectious diseases . To date, P. alecto miRNAs have not been studied yet are likely to play significant roles in important cellular functions including immunity, apoptosis, the cell cycle, inflammation, and DNA repair. In this study, 399 putative miRNAs were identified in the small RNA transcriptome of the Black flying fox, P. alecto, of which 269 had mature sequences with 100% identity to known vertebrate miRNAs in miRBase and 34 had homologs in miRBase but contained unique differences. The remaining 96 had no clear homology to known miRNAs, yet many were predicted with high confidence and appear to be genuine novel bat miRNAs.
The number of miRNAs identified in P. alecto falls broadly within the range observed for other mammals. For the 38 vertebrate species used for comparative analysis in this study, the number of miRBase records ranged from 1 to 1881. Different studies have used different tissues, different quantities of data, different methods and different parameters for identifying miRNAs, making direct comparisons difficult. In two somewhat comparable studies, miRDeep2 was used to identify 399 miRNAs in the pig intestine, including 354 with a miRDeep2 score above −3 , while in silico analysis of the horse genome identified a total of 406 miRNAs . Studies in other species of bats have reported varying numbers of precursors, the highest being 762, identified using miRanalyzer on ~20 million reads, with further confirmation provided by > 200 million additional reads . Another recent paper detected 887 mature and star miRNAs corresponding to 568 precursors in the mouse transcriptome, however this was achieved using almost 90 million Illumina reads , more than 20x the data volume used in the present study. From these comparisons it can be concluded that the number of miRNAs observed in the bat transcriptome can be considered fairly typical. For 196 of the 399 bat miRNAs, reads corresponding to both mature and star sequences were detected, further increasing the likelihood that these miRNAs result from specific miRNA biogenesis.
There is, as yet, no universally accepted procedure for identifying novel miRNAs in high-throughput sequencing data. In our study, the major factor determining the final number of miRNAs was the miRDeep2 score. As may be expected, lowering the cut-off score involves a trade-off between sensitivity (fewer false negatives) and specificity (more false positives). A cut-off score ≥ 1 provides a typical starting point for miRNA identification; however many highly-conserved miRNAs which are very likely to be real do not meet this stringent cut-off. If the cut-off score is lowered, however, the number of highly improbable candidates rapidly increases. Relying on a single, stringent cut-off point comes at a significant cost of missing many genuine miRNAs. We concluded that within our data, miRDeep2 score and read depth were the factors that best enabled miRNA identification. We chose a model that accepted all hits to known miRNAs (regardless of score), novel hits with scores ≥ 1 and read depth ≥ 2 (typical hits), and novel hits with scores ≥ −5 and read depth ≥ 5 (outliers). Following the initial screening, a further 27 candidates were culled because they overlapped higher-scoring candidates, leaving a total of 399 candidates under consideration. This model, in our opinion, reflected a better balance between sensitivity and specificity than a single parameter cut-off model. In support of our decision, we found that 48 out of the 96 miRNAs that scored less than 1 returned good BLAST hits to known vertebrate miRNAs in miRBase. The remaining 48 could not be specifically identified, but included 8 in clusters and 13 that had BLAST hits to mature miRNAs in other bats. Our further effort to prioritise novel miRNA candidates based on the combined evidence provides additional guidance to assist with selecting candidates for further study.
One miRNA (pal-can-276) featured noticeably in the analysis. Representing a homolog of miR-541, this P. alecto miRNA contained three unique changes in the mature sequence relative to other vertebrates, including two changes within the critical seed region. Amongst its predicted gene targets was ZFAT, a zinc-finger protein with roles in cell survival and apoptosis (particularly in immune-related cells) , and the TNF-receptor TNFRSF1B. In the rat, miR-541 is described as a brain-specific miRNA involved in neuron proliferation and neurite outgrowth via suppression of synapsin I , while the corresponding star sequence (which is also unique to P. alecto including one difference in the seed region), has been reported to be downregulated in a region of the brain (the spinal dorsal horn) in rats with experimentally-induced neuropathic pain . It is well established that miRNAs play important roles in apoptosis [29–31], and one possibility is that viruses such as HeV block apoptosis via induction of host miRNAs that downregulate pro-apoptotic genes. P. alecto miR-541 is a candidate miRNA in such a scenario, however it remains to be experimentally determined as to whether the differences in the sequence of P. alecto miR-541 correspond to differences in function between bats and other mammals, or whether it plays any role in apoptosis.
Four other miRNAs, representing homologs of miR-337, miR-377, miR-513c and miR-885 respectively, were also confirmed to have novel seed sequences in P. alecto. In humans, miR-513 is located within a cluster (miR-506:514) that is overexpressed in melanoma . In P. alecto, the novel miR-513 is also located within a putative cluster containing 14 other miRNAs (Figure 4b), the majority of which are novel. We note, however, that this cluster may be fast-evolving in mammals [33, 34]. MiRNAs in true genomic clusters are not only close together, but are transcribed as a polycistronic transcript from a single promoter, therefore promoter analysis may also be required to confirm genuine polycistronic clusters .
Amongst the 30 most significant novel miRNAs identified in P. alecto (Table 1), 15 were homologous to known vertebrate miRNAs but contained unique differences, while the remaining 15 could not be aligned to existing miRNAs and may represent unique or highly divergent bat miRNAs. In addition to those already discussed, several miRNAs within this set have significant functional roles in other mammals, some with potential significance to bat biology. We previously showed that in P. alecto, TP53 (p53) and MDM2 contain mutations in subcellular localization signals required for nucleocytoplasmic shuttling . MiR-215, which plays a role in the TP53:MDM2 interaction [36, 37], is unique in P. alecto and is presented in Table 1.
Bats have a concentration of positively selected genes within the oxidative phosphorylation pathway, believed to reflect adaptations necessary for flight-related energy metabolism . Our recent analysis of bat genomes revealed positive selection within the DNA damage response pathway and NF-κB pathway in bats, possibly reflecting compensatory adaptations to tolerate harmful byproducts produced by elevated metabolism . Target prediction found a putative connection between pal-can-300 (miR-874) and FANCI, which is involved in DNA repair. Other miRNAs known to be involved in oxidative phosphorylation include miR-34a  and miR-338 , however both of these are conserved in P. alecto.
Future directions involving miRNAs in the Black flying fox include analysis of differential miRNA expression, correlating miRNA expression with that of putative gene targets, and experimental validation of targets for individual miRNA candidates. Such studies will be greatly facilitated by the dataset, methods, and analysis pipeline provided in this study.
In summary, 399 putative miRNAs have been identified in the Black flying fox small RNA transcriptome. This includes a number of novel miRNAs that represent homologs of known miRNAs with functions relevant to bat biology. These findings will facilitate further studies of gene regulation in bats in relation to their role as virus reservoirs and may shed light on mechanisms underlying bat-specific traits such as flight and longevity.
High-throughput sequencing of small RNAs isolated from Pteropus alecto
Fourteen tissues (bone, brain, heart, kidney, large intestine, small intestine, liver, lung, muscle, lymph nodes, salivary glands, skin, spleen and testes) were harvested from four male Black flying foxes (P. alecto) captured in Brisbane, Queensland, Australia. All experiments were approved by the Australian Animal Health Laboratory Animal Ethics Committee. Equal amounts of tissue from each organ were pooled from the four bats and processed using the MirVana miRNA isolation kit (Ambion, Carlsbad, CA) according to the manufacturers’ guidelines. Bat small RNA was prepared for Illumina sequencing as follows: ~7 μg total RNA was size-fractionated by Novex 15% TBE-Urea gel (Invitrogen, Carlsbad, CA) and RNA fragments between 20 and 30 bases in length were isolated. The purified small RNAs were ligated with 5’ adapters (Illumina, San Diego, CA). To remove un-ligated adapters, ligation products were gel purified on Novex 15% TBE-Urea gel. Subsequently, RNA fragments were ligated with 3’ adapters (Illumina). After gel purification on Novex 10% TBE-Urea gel, fragments with adapters at both ends (70–90 bases in length) were reverse transcribed and subjected to 15 cycles of PCR. Amplification products were loaded on Novex 6% TBE gel (Invitrogen) and the gel band containing 90- to 100-bp fragments was excised. Purified cDNA was used directly for cluster generation and 36 cycles of sequencing analysis using the Illumina Cluster Station and 1G Genome Analyzer following manufacturer’s protocols.
Identification of miRNAs in deep sequencing data
The quality of Illumina deep sequencing data was examined using FastQC (version 0.9.5) . Adapters were trimmed using Cutadapt (version 1.2.1)  with the following options: −a TCGTATGCCGTCTTCTGCTTG --discard-untrimmed -m 18 -M 26 -O 10. The option -a indicates the adapter sequence to trim, while -m and -M designate the minimum and maximum length reads to keep after trimming. -O indicates the minimum number of matching bases required to trigger adapter recognition. Trimmed reads were filtered using the FASTX-Toolkit . Fastx_artifacts_filter was run with default settings to remove reads consisting of > 90% homopolymers, followed by fastq_quality_filter with the following options: −q 16 -p 90, meaning at least 90% of bases in a read needed quality scores of at least 16 for the read to be retained). Filtered reads were processed for miRNA content using miRDeep2 . Filtered reads were pre-processed using the mapper.pl module with the following options: −e -j -h -m. The -e option designates FASTQ input data, −j removes sequencing reads with called nucleotides other than A, C, G, T and N, −h converts to FASTA format and -m assigns reads with identical sequence to a single FASTA entry with a header tag designating the combined read count. Pre-processed reads were then analysed for miRNA content using the miRDeep2.pl module with the following options: mature_ref_vertebrates mature_ref_vertebrates none -a 1 -b −5. The first three fields designate reference files containing known miRNAs (mature this species, mature other species, precursor this species). In the absence of known P. alecto miRNAs, a database consisting of all known vertebrate mature miRNAs (as recorded in miRBase version 20) was constructed and used in both of the first two fields, while the third field was left intentionally blank (“none”). The option -a indicates the minimum read depth, while -b indicates the minimum miRDeep2 score for a hit to be retained (Note that novel miRNAs with only a single read were later filtered out as described below). All miRNAs identified by miRDeep2 as “known” were retained according to these settings, while “novel” miRNAs were further filtered according to the following rules: Group 1 (score ≥ 1, read depth ≥ 2), or Group 2 (score ≥ −5, read depth ≥ 5). “Novel” miRNAs that did not fall into either category were considered unreliable and were discarded.
Bioinformatics analysis of bat miRNAs
Overlapping miRNAs, miRNAs within introns, physical clustering and seed sequence usage were analysed using in-house python scripts. Non-coding RNAs (ncRNAs) were identified by RFAM batch search . Homologs were identified using standalone command line BLAST (version 2.2.29) . For comparison, vertebrate mature, star and precursor miRNA sequences were obtained from miRBase (version 20) . The vertebrate reference miRNA set consisted of 38 species, made up of 25 mammals (5 Laurasatherians, 13 primates, 3 rodents, 3 marsupials, 1 monotreme), and 13 non-mammals (10 fish, 2 birds, 1 reptile). Pteropus vampyrus and Myotis lucifugus predicted miRNA precursor sequences were obtained from Ensembl. Additional datasets were obtained from supplementary materials accompanying published articles by Biggar et al. , Platt et al. . For identification of mature and star miRNAs by BLASTN, the following options were used: −strand plus -evalue 10 -word_size 4 -penalty −4 -reward 5 -gapopen 8 -gapextend 6. MiRNAs were then categorised as 100% match, mismatch or no hit, relative to their top BLAST hits. Within the 100% match category, miRNAs were allowed to have up to 3 bp over/underhanging at the ends (a maximum of 2 bp was allowed for each individual end), relative to their top BLAST hit. This step that we refer to as ‘end-anchoring’ was accomplished using the custom BLAST output format and the command-line tool awk, and was deemed necessary to account for minor length variations in mature forms, but to screen out hits in which the alignment is merely truncated due to terminal mismatches. For identification of precursor miRNAs, the same parameters were used, except the minimum e_value was set to 0.001 and hits were filtered for > 80% identity and > 80% overlap using awk. Top hits were identified by e-value from the BLAST output using in-house python scripts. If multiple high-scoring hits were present, hits were further ranked by species in the following order (human, horse, cow, pig, dog, sheep, mouse, rat, macaque, gorilla, other) to facilitate downstream annotation analysis. Multiple sequence alignments were performed using MEGA (version 5) . Putative miRNA targets were identified using miRanda  with the following options: −en −20 -strict. The -en option defines the minimum free energy threshold for a miRNA-target pairing, while the -strict option demands perfect complementarity between the miRNA seed region and the target. A database for miR:Target prediction was constructed as follows: Using P. alecto transcriptome data assembled against the reference P. alecto genome , 3’ UTRs were extracted using a python script. Sequences were filtered to exclude those that exceeded 2500 nt in length as they may not reflect genuine mRNAs, while others lacking gene IDs were excluded on the basis that they were unsuitable for annotation enrichment analysis. Functional annotation clustering analyses (GO and KEGG) were performed using the DAVID web service  accessed via python scripts. Due to the large number of hits for GO enrichment, an additional filter of FDR < 1.0 was applied.
Availability of supporting data
The raw sequencing data have been deposited in the Sequence Read Archive (SRA) under BioProject PRJNA210946. MicroRNA sequences have been submitted to miRBase.
M.R.F. is supported by EMBO Long-Term fellowship ALTF 225–2011. We wish to acknowledge Alysha Heimberg for helpful advice regarding the identification of novel miRNAs.
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