Nucleic Acids Research Advance Access published June 13, 2016
Nucleic Acids Research, 2016 1
doi: 10.1093/nar/gkw532
microRNA editing in seed region aligns with cellular
changes in hypoxic conditions
Giovanni Nigita1,† , Mario Acunzo1,*,† , Giulia Romano1 , Dario Veneziano1 ,
Alessandro Laganà2 , Marika Vitiello3 , Dorothee Wernicke1 , Alfredo Ferro4 and Carlo
M. Croce1,*
1
Received November 09, 2015; Revised May 31, 2016; Accepted June 01, 2016
ABSTRACT
INTRODUCTION
RNA editing is a finely tuned, dynamic mechanism for
post-transcriptional gene regulation that has been
thoroughly investigated in the last decade. Nevertheless, RNA editing in non-coding RNA, such as
microRNA (miRNA), have caused great debate and
have called for deeper investigation. Until recently,
in fact, inadequate methodologies and experimental contexts have been unable to provide detailed insights for further elucidation of RNA editing affecting
miRNAs, especially in cancer.
In this work, we leverage on recent innovative
bioinformatics approaches applied to a more informative experimental context in order to analyze the
variations in miRNA seed region editing activity during a time course of a hypoxia-exposed breast cancer
cell line. By investigating its behavior in a dynamic
context, we found that miRNA editing events in the
seed region are not depended on miRNA expression,
unprecedentedly providing insights on the targetome
shifts derived from these modifications. This reveals
that miRNA editing acts under the influence of environmentally induced stimuli.
Our results show a miRNA editing activity trend
aligning with cellular pathways closely associated to
hypoxia, such as the VEGF and PI3K/Akt pathways,
providing important novel insights on this poorly elucidated phenomenon.
Low O2 tension (hypoxia) is a characteristic feature of
pathophysiological conditions such as cancer. The rapid
and uncontrolled growth of a tumor outgrows its blood supply, leaving certain regions of the cancer mass greatly deprived of the necessary oxygen intake, causing a substantial alteration of their metabolism. Hypoxic microenvironements in solid tumors lead, in fact, to the activation of several cellular pathways, such as AKT and VEGF (1), altering
the activity not only of several coding transcripts but also
of non-coding genes, such as microRNAs (Kulshreshtha et
al., 2007).
MicroRNAs (miRNAs) are endogenous, non-coding
RNA molecules, ∼22 nt long, found in eukaryotes and capable of negatively regulating gene expression at the posttranscriptional level. They represent a dominant class of
small RNAs in most somatic tissues and play important regulatory roles in most biological pathways (2).
miRNAs originate from longer precursor transcripts
called primary miRNAs (pri-miRNA) (3), processed in
the nucleus into a transitional hairpin-shaped form (premiRNA) (4). Once exported into the cytoplasm, premiRNAs are cleaved into mature miRNAs (5). Speciically,
each arm of a pre-miRNA (−5p and −3p) encodes a potential mature sequence, nevertheless only one is predominantly loaded into the RNA-induced silencing complex
(RISC) (6). In the RISC, the mature miRNA sequence allows interaction with the 3′ -untranslated region (3′ -UTR)
of mRNA targets through canonical binding, namely, a partial sequence complementarity mediated by a 6–8 nt long
region at the 5′ end of the miRNA called the ‘seed’, thus exerting its regulatory function via inhibition of protein translation or degradation of the mRNA.
* To
whom correspondence should be addressed. Tel: +1 614 292 4930; Fax: +1 614 292 3558; Email: carlo.croce@osumc.edu
Correspondence may also be addressed to Mario Acunzo. Tel: +1 614 292 1019; Fax: +1 614 292 3558; Email: mario.acunzo@osumc.edu
†
These authors contributed equally to the paper as the irst authors.
C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which
permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
journals.permissions@oup.com
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Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio
State University, Columbus, OH 43210, USA, 2 Department of Genetics and Genomic Sciences, Icahn School of
Medicine at Mount Sinai, New York City, NY 10029, USA, 3 Department of Structural & Functional Biology, University
‘Federico II’ of Naples, Naples 80126, Italy and 4 Department of Clinicaland Molecular Biomedicine, University of
Catania, Catania 95125, Italy
2 Nucleic Acids Research, 2016
cells, has also been discovered (37). On the other hand,
through a global study of ADAR1 binding, it was observed
that there may be a possible competition with DGCR8 in
pri-miRNA binding in the nucleus (38). Moreover, modulation of miRNA editing and expression by ADAR2, as well
as its tumor-promoting function, were reported (39). Finally, modiication events occurring in the mature sequence,
particularly in the seed region, could affect target recognition and modify miRNA function (24,40). Indeed, a single editing site in a MSR could drastically alter the set of
mRNA targets (41).
While RNA editing has been associated with many biological processes (25,26), including hypoxia (42–44), this has
not yet been established for miRNA editing. To investigate
potential variation in miRNA editing activity in relation
to environmental stimuli, we have analyzed small RNA-seq
(sRNA-seq) data obtained from an experiment examining
over time a hypoxia-treated breast cancer cell line, focusing
speciically on miRNA post-transcriptional modiications
occurring in the seed region, with particular attention to
A-to-I editing events. Results have indicated, for the irst
time, how the hypoxic state is accompanied by a signiicant change over time in miRNA editing activity. Finally,
by integrating variations in gene expression along with target prediction, we investigated the potential new roles which
environmental factors play on the editing of the seed region in miRNAs, thus resulting in the alteration of their respective targetomes. We have thus detected an association
between miRNA editing and fundamental biological pathways linked to hypoxia.
MATERIALS AND METHODS
Data sets used
We considered the sRNAseq data sets from Camps et al.
(45) deposited in GEO (GSE47602), comprising a timecourse (16, 32, 48 h) small RNA expression proiling of
breast adenocarcinoma cell line MCF-7 exposed to hypoxia
(1% Oxygen), along with small RNA expression proiling of
cells from the same cell line maintained in normoxic conditions (21% Oxygen). We generated all data from RNA samples obtained from two biological replicates for each experimental condition (normoxia, 16 h hypoxia, 32 h hypoxia,
48 h hypoxia).
We also considered normalized data on mRNA expression from three biological replicate samples for each of the
same experimental conditions as deposited by Camps et al.
(45) in GEO (GSE47533).
Detection and analysis of miRNA editing events
We detected miRNA editing events from the sRNAseq data
by employing the computational approach implemented by
Alon and Eisenberg (46).
Briely, we iltered all reads according to a read quality
≥20 in more than three positions. In addition, we removed
sequences identiied as 5′ or 3′ adaptors. Subsequently, we
also removed reads whose length did not fall within the typical length range for a mature miRNA (16–27 bases).
We applied the Bowtie software (v1.1.2) (47) to align
the iltered and trimmed reads against the human genome
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A single mRNA can be targeted by several miRNAs, and
a single miRNA might target hundreds of mRNAs, thus
producing so-called miRNA networks (7) that modulate
the translation of a large fraction of the transcriptome (8).
In fact, it has been shown that miRNAs are involved in
a plethora of biological processes, including development
and differentiation (9), cell cycle control (10), metabolism
(11) and apoptosis (12). It is thus not surprising that the
perturbation of miRNA networks can lead to the onset of
diseases such as metabolic disorders (13), neurodegenerative diseases (14), as well as cancer (15). In the last decade,
the emergence of a large number of studies making use of
platforms for the global assessment of miRNA expression
has enabled the identiication of speciic miRNA signatures
characteristic of speciic cancer types and subtypes (16),
along with their speciic oncogenic aberrations (17).
In the last few years, the advent of high-throughput sequencing (HTS) technology has led to a radical improvement in the accuracy and sensitivity of cancer speciications in comparison with previous expression proiling techniques, especially in relation to the detection of non-coding
RNAs (18). HTS technology is gradually becoming essential, especially in the genome-wide identiication and investigation of polymorphisms occurring in miRNA seed
regions (MSRs) as well as within target 3′ -UTRs, phenomenons that can disrupt miRNA function in many human diseases, including cancers (19–23) (http://compbio.
uthsc.edu/miRSNP/) (24).
Though previously primary emphasis was on genetic
variants to elucidate biological pathways perturbed in
human cancers, recent focus has shifted toward posttranscriptional modiications, such as RNA editing. RNA
editing is a post-transcriptional mechanism that alters the
sequence of primary RNA transcripts. A-to-I (Adenosineto-Inosine) RNA editing is the most prevalent type of
RNA editing in mammals, and is mediated by members of the family of adenosine deaminases acting on
RNA (ADAR) (25,26), which bind double-stranded RNAs
(dsRNA) (27,28), deaminating adenosine (A) to inosine (I),
which in turn is interpreted both by the splicing and translation machineries as guanosine (G) (29). A-to-I RNA editing events can occur in both coding and non-coding RNA
molecules, such as miRNAs (30,31), with 10–20% of unique
sequences potentially able to undergo A-to-I RNA editing (30,32) at the pri-miRNA level (33). While pri- or premiRNA modiications, such as A-to-I editing, outside the
mature sequence may change both the maturation (34) and
the expression of miRNAs (33). Editing by ADAR1 in primiR-455 at the +2 and +17 positions were reported to reduce the ability to bind to Drosha and then be processed
into mature miR-455-5p in human melanocytes (35). Negative regulation of ADAR1 expression mediated by CREB
in metastatic melanoma cell lines leads to an increase in
expression for miR-455-5p and, consequently, to the suppression of CPEB1, which in turn enhances melanoma
growth and metastasis in vivo (35). ADAR1 regulates the
expression of several miRNAs essential for differentiation
and neural induction in human embryonic stem cells by
acting as an RNA-binding protein (36). Another editingindependent activity of ADAR1, namely, the suppression of
miR-222 with consequent immune resistance of melanoma
Nucleic Acids Research, 2016 3
mRNA differential expression and WT\ED miRNA targets
functional enrichment analyses
We performed differential expression analysis of microarray gene expression data from publicly available
data set GSE47533, in each time-point by using the
R/Bioconductor package Limma (57). P-values were used
to rank all genes, retaining those under a signiicant threshold of 0.01 adjusted P-value (BH) (50) and included in at
least one of the targetomes of the mature miRNAs (WT and
ED) affected by the 4 statistically signiicant A-to-I editing
events analyzed above, for further functional enrichment
analysis. Furthermore, as control for the enrichment analysis, we considered the remaining DE genes not included in
the predicted targetome of the edited or unedited miRNA
in question.
We performed functional enrichment analysis on the retained set of differentially expressed (DE) genes in each
time-point by employing the software integrated pathway
analysis (IPA) by Ingenuity. Settings used included experimentally observed data for human species, speciically,
pathways exclusively associated to the MCF-7 cell line.
Cell culture, transfection and chemicals
Cells were seeded and grown in RPMI (A549) or DMEM
(HeLa) with 10% fetal bovine serum (FBS), L-glutamine
and antibiotics (Invitrogen, Carlsbad, CA, USA). All the
transfections were performed by using Lipofectamine 2000
(Invitrogen) as suggested by the manufacturer. A549 and
HeLa cells for Western blot assay were cultured to 80% conluence in six-well plates and transfected with 100 nmol of
miRNAs or negative control in a serum-free medium without antibiotics, after 6 h the medium was replaced with a
complete grown media. The cells were harvested after 48 h.
HeLa cells transfection for luciferase assay is described below.
Prediction of WT and ED miRNA targetomes
Luciferase assay
We predicted binding sites for WT and ED miRNA on the
whole 3′ UTR-ome (UCSC.hg19) through a consensus of
four miRNA target prediction tools: miRanda (v3.3a) (52),
TargetScan (v7.0) (53), PITA (v6.0) (54) and miRiam (55),
our in-house tool, enhanced with the scoring function described in (56). Standard parameters were employed for the
tools miRanda, TargetScan and PITA. miRiam’s parameters were set to detect canonical binding sites only (6mer,
7mer-A1, 7mer-m8 and 8mer), allowing no mismatches in
the seed (e.g. wobble pairs). miRiam’s scoring function is
based on the combination of the tree-based multiple linear regression learning system M5P with CTree and takes
into account six different features of miRNA/target interactions: type of seed match, miRNA nucleotide composition, pairing of the 3′ region of the miRNA, AU content of
the binding site and its lanking regions, structural accessibility of the binding site and presence of AU Rich Element
(ARE) and Cytoplasmic Polyadenylation Element (CPE)
motifs upstream of the binding sites.
We created the Venn diagrams that represent the intersection between the sets of predicted mRNA targets for the WT
and ED miRNAs by using the R package VennDiagram.
We used the luciferase reporter constructs described in another work (58). We introduced mutations in hsa-miR-27a3p-ED binding sites on the MET construct by using the
QuikChange Mutagenesis Kit (Stratagene, La Jolla, CA,
USA). We seeded HeLa cells in 12 well plate and after 24
h transfected with Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA), 1.2 g of pGL3control containing EGFR,
MET or MET mutants, 200 ng of Renilla luciferase expression construct. After 24 h from transfection, we lysed and
assayed cells with Dual Luciferase Assay (Promega) according to the manufacturer’s instructions.
The mutagenesis primers used are:
Met27ed mutFw 5’-gaccaatggcctgcagcaacactcctgtcata-3’
Met27ed mutRv 5’-tatgacaggagtgttgctgcaggccattggtc-3’
Western-blot analysis
A549 and HeLa cells were seeded and grown in appropriate media with 10% FBS in six-well plates for 24 h before
the transfection, 48 h after which we washed cells with cold
phosphate buffered saline and subjected them to lysis in lysis buffer (50 mM Tris-HCl, 1 mM EDTA, 20 g/l SDS, 5
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(UCSC hg19/GRCh37), allowing one mismatch at most,
while trimming the last two bases of the read (48) (Bowtie
parameters: -n 1 -e 70 -a -m 1 -best -strata -trim3 2). We
then mapped the total resulting number of reads against
the known pre-miRNA sequences (miRBase, release 21)
(49). All nucleotide positions in each mature or miRNA*
were screened for mismatches that were overrepresented
considering the expected sequencing error rate of 0.1% (as
consequence of applying a Phred score ilter of >30) (41).
We accomplished this by applying the binomial cumulative
distribution on the counts of each sequenced nucleotide.
We applied a very permissive expression ilter, as we took
into consideration any miRNA against which more than
ive reads were aligned. Then, we applied a Benjamini–
Hochberg false-discovery rate of 5% (50) (Benjamini and
Hochberg 1995), to detect statistically signiicant modiications, which were subsequently iltered of known single nucleotide polymorphisms (dbSNP build 142).
The editing motifs in the bases lanking the A-to-I editing sites detected, as well as sequence preference in the bases
opposing the A-to-I editing sites, were created using the WebLogo tool (51).
For each of these modiication events, we calculated the
average modiication levels (AMLs) in each condition as the
average of the modiication levels of each replicate.
We calculated a Pearson’s correlation for the series of
AMLs (as in each condition) and the series of linear foldchanges (at each time point relative to normoxia control
(45)) for each modiication event/miRNA pair, by using the
function cor.test in the stats R package.
The signiicance of the differences between editing levels
of different time points during hypoxia progression was assessed through a Wilcoxon signed rank test by employing
the function wilcox.test in the stats R package, and considering all putative A-to-I editing sites for each time point.
4 Nucleic Acids Research, 2016
mM dithiothreitol, 10 mM phenylmethylsulfonyl luoride).
Equal amounts of protein lysates (50 g each) and rainbow
molecular weight marker (Bio-Rad Laboratories, Hercules,
CA, USA) were separated by 4–20% SDS–PAGE and then
electrotransferred to nitrocellulose membranes. The membranes were blocked with a buffer containing 5% non-fat
dry milk in Tris-buffered saline with 0.1% Tween-20 for 2
h and incubated overnight with antibodies at 4◦ C. After a
second wash with Tris-buffered saline with 0.1% Tween 20,
the membranes were incubated with peroxidase-conjugated
secondary antibodies (GE Healthcare, Amersham, Pittsburg, PA, USA) and developed with an enhanced chemiluminescence detection kit (Pierce, Rockford, IL, USA).
MET was obtained from Cell signaling (#4560), EGFR
from Santa Cruz (#Sc-03), Tubulin from Sigma (#T6199).
Total RNA was extracted with TRIzol solution (Invitrogen,
Carlsbad, CA, USA), according to the manufacturer’s instructions.
Q-real-time PCR (Q-rt PCR)
For the detection of single-target a-miRs, we performed
quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) by using a standard TaqMan PCR Kit
protocol on an Applied Biosystem 7900HT Sequence Detection System (Applied Biosystems, Carlsbad, CA, USA).
For the TaqMan qRT, the 10 l PCRreaction included 0.67
ml RT product, 1 ml TaqMan Universal PCR Master Mix
(Applied Biosystems, Carlsbad, CA, USA), 0.2 mM Taqman probe, 1.5 mM forward primer and 0.7 mM reverse
primer. The reactions were incubated in a 96-well plate at
95◦ C for 10 min, followed by 40 cycles of 95◦ C for 15 s and
60◦ C for 1 min. All reactions ran in triplicate. The threshold cycle (Ct) is deined as the fractional cycle number at
which the luorescence passes the ixed threshold. The comparative Ct method for relative quantization of gene expression (Applied Biosystems, Carlsbad, CA, USA) was used to
determine miRs expression levels. The y-axis represents the
relative expression of the different miRs expression was calculated relative to U44 rRNA. We carried out experiments
in triplicate for each data point, and performed data analysis by using software tools (Bio-Rad Laboratories, Hercules,
CA, USA).
RESULTS
Systematic identiication of miRNA-sequence modiications
in human breast cancer cells during a time-course of exposure
to hypoxic conditions
The advent of high-throughput sequencing technology has
considerably improved the exploration of the transcriptome. HTS has, indeed, allowed a more precise analysis of
differential expression under different conditions and the
detection of different types of sequence modiications in
transcripts, such as those caused by RNA editing. Recently,
Camps et al. studied in depth the regulation of miRNA expression in the human breast cancer cell line MCF-7 during
hypoxia (45), identifying 41 and 28 miRNAs signiicantly
Levels of miRNA modiications in the seed region change during hypoxia
Given the lack of substantial knowledge on the matter, we
sought to determine whether the level of miRNA modiication events changes during a dynamic cellular context, such
as in a hypoxia time course, and whether such changes are
proportionally associated to miRNA expression or not. To
accomplish this, we considered only those miRNA modiication events occurring in the seed region in both biological
replicates to assure robustness to our analysis. Such was the
case for 7 of the statistically signiicant modiication events,
5 of which are A-to-G (all in MSRs) (Figure 1).
The AMLs of the majority of miRNAs affected (5 out
of 7) generally increased with time of exposure to hypoxia
(Figure 1A and B), a result which is in line with relatively recent indings where higher post-trascriptional modiication
rates in coding genes are detected under increased exposure to hypoxia (42,43,64). Nevertheless, we also observed
decreased (e.g. A-to-G modiications in miR-421) and null
levels (e.g. non A-to-G modiications in miR-425-5p) of the
editing activity. To relate the level of modiications with dif-
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Antibody used for Western-blot analysis and RNA extraction
up- and down-regulated, respectively. Leveraging on their
published data (GEO reference: GSE47534; miRNA-seq:
GSE47602), including sequences of a small RNA library for
HTS originating from biological duplicate RNA samples
for each experimental condition (normoxia, 16, 32 and 48 h
hypoxia), to investigate the presence of post-transcriptional
modiication events in miRNA mature sequences and evaluate such changes as they occur in the hypoxic cellular context. To systematically identify such events, we have employed the Alon–Eisenberg pipeline (41,46) (see Supplementary Figure S1 and Material and Methods section), currently the only method for accurate detection and quantiication of canonical and non-canonical editing sites in mature miRNAs from HTS data (59). We identiied a total of
31 statistically signiicant modiication sites in 21 different
miRNAs, with 7 sites previously known (see Supplementary
Figure S2). Interestingly, 83% of A-to-G events (10 of 12)
occurred in seed regions compared to 37% of non A-to-G
events (7 of 19), as shown in Supplementary Table S1 and
Supplementary Figure S3. Since post-transcriptional A-toG modiications are mostly expected to be the result of Ato-I editing (29), it is of relevant importance to observe a signiicant incidence of such modiications in the miRNA seed
region. This reveals how the A-to-I editing phenomenon
may preferentially occur in the miRNA seed region under
hypoxic cellular conditions, thus causing signiicant shifts
in the miRNA targetome (24,40).
The discovery of these A-to-I editing sites in pre-miRNAs
(Supplementary Figure S4) was further supported by the
presence of speciic editing motifs near the sites, detected
by neighborhood proiling, in line with previous studies
(60,61). As shown in Supplementary Figure S5A, changes
to nucleotides C and U are over-represented upstream of
the edited site, while G is under-represented upstream and
over-represented downstream of the edited site, as previous
studies have established (62,63). The nucleotide opposing
the editing site is U (see Supplementary Figure S5B), as previously documented (41).
Nucleic Acids Research, 2016 5
ferential expression under hypoxia, we calculated Pearson’s
correlation (Figure 1 and Material and Methods section)
for the AML in each condition and the linear fold-change
obtained at each time point relative to normoxia control
(45). Interestingly, there is no signiicant correlation among
the AMLs and the linear fold changes shows that miRNA
seed sequence modiications are at least not positively correlated with miRNA expression (Figure 1). This suggests
that the trend of the miRNA seed sequence modiication
phenomenon does not follow miRNA expression during dynamic cellular changes, such as those occurring during progressive hypoxia. Additionally, in order to speciically assess
the signiicance of the differences between editing levels of
different time points during hypoxia progression, we performed a Wilcoxon signed rank test considering all putative
A-to-I editing sites for each time point, as presented in Figure 1A. Results have shown a signiicant increase in editing
levels only between the 16 h and 48 h hypoxia time points
(P-value < 0.05).
miRNA targetome changes due to A-to-I seed region editing
during hypoxia
To estimate the impact of editing on miRNA function during hypoxia progression, we selected to investigate A-to-G
miRNA modiications, as these are expected to be the result
of A-to-I editing. Speciically, we decided to consider all Ato-I editing events (Figure 1A, Supplementary Figure S3)
occurring within MSRs (6-mers comprising nucleotide positions 2–7 corresponding to the seed region, thus excluding hsa-miR-27a-5p whose putative A-to-I editing site occurs in position 1), extended by nucleotide position 8 (in
order to also take into account offset 6-mer sites comprising nucleotide positions 3–8), as the fundamental role of
the 6-mer seed sequence in the miRNA–mRNA interaction has been widely documented (65). A single editing site
in the MSR could determine important changes, as shown
in Supplementary Figure S6. In fact, by comparing edited
miRNA seed regions with their un-edited references (49),
there are two possible scenarios for an edited miRNA: ei-
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Figure 1. Average modiication levels have been calculated for each replicate in normoxia and in each hypoxia time point. (A) The colors used for the rows
are summarized in the ‘volume’ icon on the left of the table in the igure, with green representing low levels and red representing high levels. Fold-changes (in
linear scale) for each microRNA at each time point relative to normoxia control have been reported as obtained by Camps et al. (45) (clear rows). Pearson’s
correlation (r), with relative P-value, was calculated between the average modiication level and the linear fold-change for each miRNA. (B) Plotting of
observed average modiication levels over time-course for each selected A-to-I edited miRNA.
6 Nucleic Acids Research, 2016
WT binding site, we performed deletion of this predicted
binding site within the MET 3′ -UTR (Figure 3A). As shown
in Figure 3C, miR-27a-3p ED could still regulate the luciferase expression of MET 3′ -UTR, despite the mutation
of miR-27a-3p WT binding site. Subsequently, we mutated
by deletion the predicted binding site for miR-27a-3p ED
on the MET 3′ UTR (Figure 3A). As shown in Figure 3C,
miR-27a-3p ED was no longer able to repress the luciferase
expression of MET 3′ -UTR with miR-27a-3p ED binding
site mutated. That proves the binding site of the ED version
of the miRNA to be the only functional one, conirming our
prediction. We also predicted that miR-27a-3p ED would
no longer be able to directly target the EGFR 3′ -UTR, as
shown in Supplementary Figure S7. To validate this prediction, we co-transfected HeLa cells with both miR-27a-3p
ED and the EGFR 3′ -UTR containing both the WT and
ED miR-27a-3p binding sites, previously cloned into the
pGL3 control vector downstream of the luciferase ORF. As
a result, we noted that miR-27a-3p ED could not inhibit the
luciferase activity of the EGFR 3′ UTR compared to negative control (Figure 3D). In addition, the over-expression
of miR-27a-3p ED reduced the endogenous MET level but
could no longer reduce the endogenous EGFR protein level
in A549 and HeLa cells compared to control (Figure 3E, F
and Supplementary Figure S8). We conirmed the increased
expression of WT and ED miR-27a-3p in transfected cells
by RT-qPCR (Figure 3G). We also conirmed the EGFR
expression trend in the hypoxic samples in which the above
miRNA editing analysis was performed. The EGFR expression shown in Figure 3H shows its increase during the hypoxia time course. These results thus conirm our target
predictions as displayed in Figure 2, while also speciically
showing that the increase in A-to-I editing of miR-27a-3p is
in accord with increased EGFR expression during hypoxia.
miRNA editing is in line with dynamic phenotype alteration
The hypoxia-Inducible Factor 1 (HIF-1), a key factor in cellular hypoxia response, is known to regulate speciic genes
and inluence several cellular pathways (69). For instance,
the VEGF and PI3K/Akt pathways are closely associated
with the hypoxic condition and are activated during this cellular process (70–72).
After having analyzed the RNA-seq data, we proceeded
to perform differential gene expression analysis on microarray data also provided by Camps et al. (GEO reference:
GSE47534; mRNA: GSE47533) (45), originating from the
same hypoxia time-course samples. We then applied statistical signiicance iltering, by considering only genes which
were differentially expressed with an adjusted P-value below 0.01. Subsequently, at each time point, for each miRNA
with A-to-I editing events occurring in their MSRs (2–8 nt
seed region) we isolated their predicted targets as present
in the DE gene set (namely, the set of targets exclusive to
the WT and ED versions, respectively, as well as shared targets); on the other hand, we also considered the DE genes
which were not predicted to be targets of either version
of the considered miRNA, as depicted in Figure 4A. We
thus performed an MCF7-speciic functional enrichment
of both subsets of genes by Ingenuity Pathway Analyzer
software. Finally, we focused on hypoxia-related pathways,
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ther it is modiied into a new miRNA (acquiring an entirely
unprecedented seed sequence) or its seed sequence is modiied into that of another known human miRNA (Supplementary Figure S6).
Subsequently, in order to compare the target sets of the
wild type (WT) and edited (ED) versions of each miRNA,
we performed a robust binding site prediction analysis on
gene 3′ -UTRs with a consensus of four miRNA target prediction tools, namely, miRanda (52), TargetScan (53), PITA
(54) and miRiam, our in-house tool, enhanced with our recently developed scoring function which takes into account
both sequence and structure features (55,56). The prediction tools we employed consider seeds when evaluating target genes, starting from a 6-mer MSRs within nucleotide positions 2–8 (as stated above and as the established norm for
miRNA target prediction tools). Any modiication occurring outside such MSRs, under such paradigm, would thus
not imply any change in the predicted targetome, contributing, at best, to a strengthening or weakening of an already
established potential binding site.
In line with previous studies, we performed target predictions for A-to-I edited miRNAs by considering inosine
as guanosine (41,66). In fact, inosine can bind cytidine
with approximately the same energy as guanosine, while the
binding of inosine to uridine is weaker than that of guanosine. Moreover, unlike guanosine, inosine can bind weakly
to adenosine (67). As shown in Figure 2, the sets of predicted mRNA targets of the WT and A-to-I ED miRNAs
respectively, overlap with an average of 19%, a surprisingly
larger percentage compared to the 3% known so far (41).
These results are consistent with a very recent study (68), in
which Hill et al. have demonstrated that a single nucleotide
substitution in positions 2–8 of a mature miRNA can determine a targetome shift >60–70%. In fact, the considered
editing events may lead to an average loss >60% of mRNA
targets for WT miRNAs when edited. In particular, we predicted drastic changes for miR-27a-3p and miR-421, suggesting alteration of their functions.
In light of previous reports which proved that the seed
of miR-27a-3p possesses binding matches in the 3′ -UTR of
human MET (nucleotide 1564–1571; NM 000245) and human EGFR (nucleotide 200–207 and nucleotide 430–436
NM 005228) (58), we decided to employ this knowledge
to validate our target prediction. Moreover, the choice of
miR-27a-3p has allowed us to validate our prediction results in relation to two speciic and conirming contexts: that
of complete loss of targeting (as with miR-27a-3p ED on
EGFR) as well as that of replacement of an old binding site
with a new one (as with miR-27a-3p ED and MET) (Supplementary Figure S7).
To verify that MET was still a direct target of miR-27a-3p
ED, we cloned the MET 3′ UTR containing the predicted
WT and ED binding sites for miR-27a-3p, into the pGL3
control vector, downstream of the luciferase open reading
frame (ORF) (Figure 3A). Transfecting HeLa cells with WT
and ED miR-27a-3p, respectively, together with the MET
3′ -UTR luciferase construct, resulted in a signiicant inhibition of luciferase activity in both cases as compared to
the negative control, conirming our prediction (Figure 3B).
To determine that miR-27a-3p ED was not affecting the
MET 3′ -UTR by nonspeciic binding to the miR-27a-3p
Nucleic Acids Research, 2016 7
such as VEGF and PI3K, to evaluate how the miRNA editing phenomenon globally behaves in relation to these pathways during the hypoxic time course, by confronting the
set of targets of the WT miRNA with the set of targets of
its ED version, using as control all those DE transcripts
which were not predicted to be targets of either version. Our
data clearly shows how A-to-I editing of the MSR of these
miRNAs translates into a diminished ability to target key
genes involved in these two important pathways throughout
the full time course of the observed hypoxia process (Figure 4B, Supplementary Figure S9 and Supplementary Table
S2). Our results unprecedentedly show that A-to-I miRNA
editing is not a merely random phenomenon, but rather
a molecular mechanism that, speciically through miRNA
seed mutation events, is in line with important biological
processes.
DISCUSSION
Until recently, miRNA editing has been at the center of a
debate concerning its purpose and even its very occurrence.
After several reports attempted to shed light on the matter, discordant opinions and widespread scepticism on the
topic seem to have started to fade in favor of afirming the
existence of such biological phenomenon (41,73). Additionally, all studies conducted on the matter have investigated
miRNA editing under static cellular states, performing a
comparison analysis between ixed pathological (i.e. cancerous) and normal conditions (39). This type of study provides insights on on what changes without elucidating how
miRNA editing behaves.
Recently, the advent of innovative bioinformatics approaches have allowed for an unprecedentedly precise evaluation of miRNA editing events from deep sequencing data
(41,46). Combining such novel methodologies with the nature of HTS technology, has, for instance, presented the opportunity to investigate the phenomenon under speciic cellular states over time, providing a dynamic view which is
necessary for a more informative analysis and understanding of the phenomenon of RNA editing.
In the present study, we have indeed applied the recently published bioinformatics pipeline devised by Alon
and Eisenberg to time course sRNA-seq data originating
from MCF-7 breast cancer cells cultured under hypoxic
conditions, to globally analyze the miRNA editing phenomenon in a dynamic cellular context. Indeed, the main
core of our work focuses on evaluating how the miRNA
editing activity varies in relation to hypoxia progression,
and, secondly, to elucidate how this phenomenon globally
relates to cellular changes. As we have observed, the general miRNA seed region modiication level varied signiicantly as well as proved to be signiicantly independent from
miRNA expression during the hypoxic time course.
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Figure 2. Venn diagrams of predicted mRNA target sets for the WT and A-to-I ED miRNAs.
8 Nucleic Acids Research, 2016
Our research thus sought to speciically investigate three
possibilities regarding the phenomenon: miRNA editing
could have indeed displayed a collective behavior which resulted in line with, contrary to, or independent of the cellular response to external factors. In order to accomplish this,
we focused on editing events occurring in the MSR as it is
fundamental for targeting effectiveness and speciicity (65).
Target prediction for both WT and ED miRNAs was followed by experimental validation. Interestingly, functional
enrichment analysis of four putative A-to-I edited miRNAs
revealed a common trend during hypoxia. In fact, according to our observations as presented in Figure 4, we have
detected a collective alignment of the targetome shifts for
all four MSR-edited miRNAs which were predicted to no
longer target key genes of hypoxia-related pathways such
as VEGF and PI3K/AKT, as summarized in Supplementary Figure S9 and Table S2. This shows a global disinterest of miRNA editing in affecting hypoxia-related pathways.
This does not necessarily signify that miRNA editing, due
to very low editing levels, plays an active role, rather that
there is a non-random trend of disinterest. Future studies
on other tissue and cellular contexts more quantitatively
informative for the editing phenomenon (i.e. glioblastoma)
could provide further conirmation on the matter. Nonetheless, our goal is to establish a novel and more appropriate
approach of investigation of this largely unexplored phe-
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Figure 3. Effects of A-to-I miRNA seed editing on targeting. (A) c-MET 3′ UTR binding sites for miR-27a-3p, ED (yellow spot) and WT (blue spot), along
with corresponding deletions (lightening bolt). (B) Luciferase assay for pGL3-MET 3′ UTR WT construct co-transfected with miR-27a-3p WT, miR-27a3p ED or negative scramble miRNA control (Scr) in HeLa cells (error bars: ±). (C) Luciferase assay for pGL3-MET 3′ UTR WT/mut and pGL3-MET 3′
UTR ED/mut constructs co-transfected with miR-27a3p ED or negative control (Scr) in HeLa cells. (D) Luciferase assay for pGL3-EGFR 3′ UTR WT
construct co-transfected with miR-27a-3p WT, miR-27a-3p ED or negative controls (Scr) in HeLa cells. (E and F) c-MET and EGFR expression by western
blot in A549 cells transfected with miR-27a-3p ED, miR-27a-3p WT or negative control (Scr) and harvested after 48 h, with graphs for c-Met/Tubulin or
EGFR/Tubulin ratio quantiication. (G) qRT-PCR of WT and ED miR-27a-3p respectively after miR transfection in A549 cells as control for Figure 3E
and F. (H) EGFR expression as in (45).
Nucleic Acids Research, 2016 9
nomenon, with regards to its behavior in relation to functional cellular changes. This allows to elucidate how the
ine-tuning miRNA editing activity locates itself within the
life of the cell, especially in light of external stimuli.
The current study is also the irst to investigate the biological behavior of miRNA editing within a global and
dynamic context, with the hope of providing new elements
to further elucidate the cellular role of this biological phenomenon in the near future. The dynamic dimension of
our study is, in our view, essential to better assess the nature of the molecular phenomenon. In addition to clarifying
its purposeful existence, we were able to successfully relate
miRNA editing to a biological condition such as hypoxia,
along a time frame. It should be noted that hypoxia represents but a limited instance of a much wider panorama, in
which miRNA editing could be considered as a component
of a response mechanism employed by the cell to rapidly
shift the miRNA targetome according to contextual needs,
especially in reaction to stressful external factors. The evidence that emerges from our functional data shows, indeed,
how the transformation (editing) of certain miRNAs is associated with the cellular response to hypoxic stimulus. Additionally, despite the low editing levels, we do not intend
to focus on the editing of a single miRNA, rather consider
the phenomenon globally so to evaluate its collective effects
and behavior. Adding a temporal dimension to the study of
this phenomenon provides a quantity and quality of information on how the cell responds to cellular changes. Specifically, miRNA editing has thus shown to align itself to cellular needs and thus, potentially contribute to cell economy.
In fact, instead of transcribing novel miRNAs, the cell can
more easily leverage on the already existing population.
Unfortunately, lack of proper investigation of editing
events in the seed region of miRNA binding sites on mRNAs, and of editing affecting pre-miRNA maturation due
to limitations in current technology, do not yet allow for
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Figure 4. Functional enrichment of differentially expressed targets. (A) General scheme representing the functional enrichment worklow: differentially
expressed genes with a signiicant adjusted P-value (BH < 0.01) in each hypoxia time point relative to normoxia were separated according to whether or
not belonging to predicted miRNA targetome (WT or ED) for each miRNA, followed by MCF7-speciic functional enrichment by Ingenuity Pathway
Analyzer (IPA) software. (B) IPA analysis on VEGF and PI3K/Akt pathways in MCF7 cell line. Graphs show -log (P-value) over time course for both WT
(purple) and A-to-I ED (green) predicted miRNA targets. Bars represent the level of signiicance for the indirect involvement in the considered pathways
for WT and A-to-I ED miRNAs, respectively. P-values represent the signiicance of the set of target genes (which and how many) involved in a given
pathway for each miRNA (WT and ED, respectively). The red dotted line represents the signiicance threshold level (-log(p), where P = 0.05).
10 Nucleic Acids Research, 2016
a more comprehensive and detailed evaluation of the phenomenon. Nevertheless, our results provide a foundation
for further elucidation of miRNA editing, giving a direction
to future innovative investigation of its biological importance and potential involvement in physiological and pathological cellular changes.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENT
FUNDING
National Institutes of Health (NIH) [U01-CA152758 to
M.A. and C.M.C]; Italian Foundation for Cancer Research
(FIRC) [15046 to G.N.]; Italian Funding for Cancer Research (FIRC) [16572 to D.V.].
Conlict of interest statement. None declared.
REFERENCES
1. Namiki,A., Brogi,E., Kearney,M., Kim,E.A., Wu,T., Coufinhal,T.,
Varticovski,L. and Isner,J.M. (1995) Hypoxia induces vascular
endothelial growth factor in cultured human endothelial cells. J. Biol.
Chem., 270, 31189–31195.
2. Ameres,S.L. and Zamore,P.D. (2013) Diversifying microRNA
sequence and function. Nat. Rev. Mol. Cell Biol., 14, 475–488.
3. Bartel,D.P. (2004) MicroRNAs: genomics, biogenesis, mechanism,
and function. Cell, 116, 281–297.
4. Gregory,R.I., Chendrimada,T.P. and Shiekhattar,R. (2006)
MicroRNA biogenesis: isolation and characterization of the
microprocessor complex. Methods Mol. Biol., 342, 33–47.
5. Lund,E. and Dahlberg,J.E. (2006) Substrate selectivity of exportin 5
and Dicer in the biogenesis of microRNAs. Cold Spring Harb. Symp.
Quant. Biol., 71, 59–66.
6. Schwarz,D.S. and Zamore,P.D. (2002) Why do miRNAs live in the
miRNP? Genes Dev., 16, 1025–1031.
7. Volinia,S., Galasso,M., Costinean,S., Tagliavini,L., Gamberoni,G.,
Drusco,A., Marchesini,J., Mascellani,N., Sana,M.E., Abu Jarour,R.
et al. (2010) Reprogramming of miRNA networks in cancer and
leukemia. Genome Res., 20, 589–599.
8. Friedman,R.C., Farh,K.K.-H., Burge,C.B. and Bartel,D.P. (2009)
Most mammalian mRNAs are conserved targets of microRNAs.
Genome Res., 19, 92–105.
9. Harfe,B.D. (2005) MicroRNAs in vertebrate development. Curr.
Opin. Genet. Dev., 15, 410–415.
10. Carleton,M., Cleary,M.A. and Linsley,P.S. (2007) MicroRNAs and
cell cycle regulation. Cell Cycle, 6, 2127–2137.
11. Boehm,M. and Slack,F.J. (2006) MicroRNA control of lifespan and
metabolism. Cell Cycle, 5, 837–840.
12. Jovanovic,M. and Hengartner,M.O. (2006) miRNAs and apoptosis:
RNAs to die for. Oncogene, 25, 6176–6187.
13. Poy,M.N., Eliasson,L., Krutzfeldt,J., Kuwajima,S., Ma,X.,
Macdonald,P.E., Pfeffer,S., Tuschl,T., Rajewsky,N., Rorsman,P. et al.
(2004) A pancreatic islet-speciic microRNA regulates insulin
secretion. Nature, 432, 226–230.
14. Jin,P., Alisch,R.S. and Warren,S.T. (2004) RNA and microRNAs in
fragile X mental retardation. Nat. Cell Biol., 6, 1048–1053.
15. Iorio,M.V. and Croce,C.M. (2009) MicroRNAs in cancer: small
molecules with a huge impact. J. Clin. Oncol., 27, 5848–5856.
Downloaded from http://nar.oxfordjournals.org/ at OHIO STATE UNIVERSITY LIBRARIES on June 17, 2016
The authors would like to thank Prof. F. Kay Hubner for her
useful support during the drafting process and Dr Shahar
Alon for his precious suggestions during the application of
the computational approach for RNA editing detection.
16. Lehmann,B.D., Bauer,J.A., Chen,X., Sanders,M.E.,
Chakravarthy,A.B., Shyr,Y. and Pietenpol,J.A. (2011) Identiication
of human triple-negative breast cancer subtypes and preclinical
models for selection of targeted therapies. J. Clin. Invest., 121,
2750–2767.
17. Iorio,M.V. and Croce,C.M. (2012) microRNA involvement in human
cancer. Carcinogenesis, 33, 1126–1133.
18. Veneziano,D., Nigita,G. and Ferro,A. (2015) Computational
approaches for the analysis of ncRNA through deep sequencing
techniques. Front. Bioeng. Biotechnol., 3, 1–6.
19. Calin,G.A., Ferracin,M., Cimmino,A., Di Leva,G., Shimizu,M.,
Wojcik,S.E., Iorio,M.V., Visone,R., Sever,N.I., Fabbri,M. et al. (2005)
A MicroRNA signature associated with prognosis and progression in
chronic lymphocytic leukemia. N. Engl. J. Med., 353, 1793–1801.
20. Gao,Y., He,Y., Ding,J., Wu,K., Hu,B., Liu,Y., Wu,Y., Guo,B.,
Shen,Y., Landi,D. et al. (2009) An insertion/deletion polymorphism
at miRNA-122-binding site in the interleukin-1alpha 3’ untranslated
region confers risk for hepatocellular carcinoma. Carcinogenesis, 30,
2064–2069.
21. Yue,C., Wang,M., Ding,B., Wang,W., Fu,S., Zhou,D., Zhang,Z. and
Han,S. (2011) Polymorphism of the pre-miR-146a is associated with
risk of cervical cancer in a Chinese population. Gynecol. Oncol., 122,
33–37.
22. Nicoloso,M.S., Sun,H., Spizzo,R., Kim,H., Wickramasinghe,P.,
Shimizu,M., Wojcik,S.E., Ferdin,J., Kunej,T., Xiao,L. et al. (2010)
Single-nucleotide polymorphisms inside microRNA target sites
inluence tumor susceptibility. Cancer Res., 70, 2789–2798.
23. Brewster,B.L., Rossiello,F., French,J.D., Edwards,S.L., Wong,M.,
Wronski,A., Whiley,P., Waddell,N., Chen,X., Bove,B. et al. (2012)
Identiication of ifteen novel germline variants in the BRCA1 3’UTR
reveals a variant in a breast cancer case that introduces a functional
miR-103 target site. Hum. Mutat., 33, 1665–1675.
24. Bhattacharya,A., Ziebarth,J.D. and Cui,Y. (2014) PolymiRTS
Database 3.0: linking polymorphisms in microRNAs and their target
sites with human diseases and biological pathways. Nucleic Acids
Res., 42, D86–D91.
25. Nishikura,K. (2010) Functions and regulation of RNA editing by
ADAR deaminases. Annu. Rev. Biochem., 79, 321–349.
26. Nishikura,K. (2015) A-to-I editing of coding and non-coding RNAs
by ADARs. Nat. Rev. Mol. Cell Biol., 17, 83–96.
27. Bass,B.L. (2002) RNA editing by adenosine deaminases that act on
RNA. Annu. Rev. Biochem., 71, 817–846.
28. Jepson,J.E.C. and Reenan,R.A. (2008) RNA editing in regulating
gene expression in the brain. Biochim. Biophys. Acta, 1779, 459–470.
29. Rueter,S.M., Dawson,T.R. and Emeson,R.B. (1999) Regulation of
alternative splicing by RNA editing. Nature, 399, 75–80.
30. Kawahara,Y., Megraw,M., Kreider,E., Iizasa,H., Valente,L.,
Hatzigeorgiou,A.G. and Nishikura,K. (2008) Frequency and fate of
microRNA editing in human brain. Nucleic Acids Res., 36,
5270–5280.
31. Kawahara,Y. (2012) Quantiication of adenosine-to-inosine editing of
microRNAs using a conventional method. Nat Protoc, 7, 1426–1437.
32. Blow,M.J., Grocock,R.J., Van Dongen,S., Enright,A.J., Dicks,E.,
Futreal,P.A., Wooster,R. and Stratton,M.R. (2006) RNA editing of
human microRNAs. Genome Biol., 7, R27.
33. Yang,W., Chendrimada,T.P., Wang,Q., Higuchi,M., Seeburg,P.H.,
Shiekhattar,R. and Nishikura,K. (2006) Modulation of microRNA
processing and expression through RNA editing by ADAR
deaminases. Nat. Struct. Mol. Biol., 13, 13–21.
34. Chawla,G. and Sokol,N.S. (2014) ADAR mediates differential
expression of polycistronic microRNAs. Nucleic Acids Res., 42,
5245–5255.
35. Shoshan,E., Mobley,A.K., Braeuer,R.R., Kamiya,T., Huang,L.,
Vasquez,M.E., Salameh,A., Lee,H.J., Kim,S.J., Ivan,C. et al. (2015)
Reduced adenosine-to-inosine miR-455-5p editing promotes
melanoma growth and metastasis. Nat. Cell Biol., 17, 311–321.
36. Chen,T., Xiang,J.-F., Zhu,S., Chen,S., Yin,Q.-F., Zhang,X.-O.,
Zhang,J., Feng,H., Dong,R., Li,X.-J. et al. (2015) ADAR1 is required
for differentiation and neural induction by regulating microRNA
processing in a catalytically independent manner. Cell Research, 25,
459–476.
37. Galore-Haskel,G., Nemlich,Y., Greenberg,E., Ashkenazi,S.,
Hakim,M., Itzhaki,O., Shoshani,N., Shapira-Fromer,R.,
Ben-Ami,E., Ofek,E. et al. (2015) A novel immune resistance
Nucleic Acids Research, 2016 11
38.
39.
40.
41.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
thermodynamics and empirical constraints. J. RNAi Gene Silencing,
6, 379–385.
Laganà,A., Acunzo,M., Romano,G., Pulvirenti,A., Veneziano,D.,
Cascione,L., Giugno,R., Gasparini,P., Shasha,D., Ferro,A. et al.
(2014) miR-Synth: a computational resource for the design of
multi-site multi-target synthetic miRNAs. Nucleic Acids Res., 42,
5416–5425.
Smyth,G.K. (2005) Limma: linear models for microarray data. In:
Gentleman,RVC, Dudoit,S, Irizarry,R and Huber,W (eds).
Bioinformatics and Computational Biology Solutions using R and
Bioconductor. Springer, NY.
Acunzo,M., Romano,G., Palmieri,D., Laganà,A., Garofalo,M.,
Balatti,V., Drusco,A., Chiariello,M., Nana-Sinkam,P. and
Croce,C.M. (2013) Cross-talk between MET and EGFR in non-small
cell lung cancer involves miR-27a and Sprouty2. Proc. Natl. Acad.
Sci. U.S.A., 110, 8573–8578.
Nigita,G., Veneziano,D. and Ferro,A. (2015) A-to-I RNA editing:
Current knowledge sources and computational approaches with
special emphasis on non-coding RNA molecules. Front. Bioeng.
Biotechnol., 3, 1–7.
Kleinberger,Y. and Eisenberg,E. (2010) Large-scale analysis of
structural, sequence and thermodynamic characteristics of A-to-I
RNA editing sites in human Alu repeats. BMC Genomics, 11, 1–17.
Nigita,G., Alaimo,S., Ferro,A., Giugno,R. and Pulvirenti,A. (2015)
Knowledge in the investigation of A-to-I RNA editing signals. Front.
Bioeng. Biotechnol., 3, 1-8.
Polson,A.G. and Bass,B.L. (1994) Preferential selection of adenosines
for modiication by double-stranded RNA adenosine deaminase.
EMBO J., 13, 5701–5711.
Lehmann,K.A. and Bass,B.L. (2000) Double-stranded RNA
adenosine deaminases ADAR1 and ADAR2 have overlapping
speciicities †. Biochemistry, 39, 12875–12884.
Fang,X., Nevo,E., Han,L., Levanon,E.Y., Zhao,J., Avivi,A.,
Larkin,D., Jiang,X., Feranchuk,S., Zhu,Y. et al. (2014) Genome-wide
adaptive complexes to underground stresses in blind mole rats
Spalax. Nat. Commun., 5, 1–9.
Bartel,D.P. (2009) MicroRNAs: target recognition and regulatory
functions. Cell, 136, 215–233.
Kawahara,Y., Zinshteyn,B., Sethupathy,P., Iizasa,H.,
Hatzigeorgiou,A.G. and Nishikura,K. (2007) Redirection of silencing
targets by adenosine-to-inosine editing of miRNAs. Science, 315,
1137–1140.
Vendeix,F.A.P., Munoz,A.M. and Agris,P.F. (2009) Free energy
calculation of modiied base-pair formation in explicit solvent: A
predictive model. RNA, 15, 2278–2287.
Hill,C.G., Jabbari,N., Matyunina,L.V. and McDonald,J.F. (2014)
Functional and evolutionary signiicance of human microRNA seed
region mutations. PLoS One, 9, e115241.
Semenza,G.L. (2013) HIF-1 mediates metabolic responses to
intratumoral hypoxia and oncogenic mutations. J. Clin. Invest., 123,
3664–3671.
Hirota,K. and Semenza,G.L. (2006) Regulation of angiogenesis by
hypoxia-inducible factor 1. Crit. Rev. Oncol./Hematol., 59, 15–26.
Liao,D. and Johnson,R.S. (2007) Hypoxia: a key regulator of
angiogenesis in cancer. Cancer Metastasis Rev., 26, 281–290.
Coleman,M.L. and Ratcliffe,P.J. (2009) Angiogenesis: escape from
hypoxia. Nat. Med., 15, 491–493.
de Hoon,M.J.L., Taft,R.J., Hashimoto,T., Kanamori-Katayama,M.,
Kawaji,H., Kawano,M., Kishima,M., Lassmann,T., Faulkner,G.J.,
Mattick,J.S. et al. (2010) Cross-mapping and the identiication of
editing sites in mature microRNAs in high-throughput sequencing
libraries. Genome Res., 20, 257–264.
Downloaded from http://nar.oxfordjournals.org/ at OHIO STATE UNIVERSITY LIBRARIES on June 17, 2016
42.
mechanism of melanoma cells controlled by the ADAR1 enzyme.
Oncotarget, 6, 28999–29015.
Bahn,J.H., Ahn,J., Lin,X., Zhang,Q., Lee,J.-H., Civelek,M. and
Xiao,X. (2015) Genomic analysis of ADAR1 binding and its
involvement in multiple RNA processing pathways. Nat. Commun., 6,
1–13.
Tomaselli,S., Galeano,F., Alon,S., Raho,S., Galardi,S., Polito,V.,
Presutti,C., Vincenti,S., Eisenberg,E., Locatelli,F. et al. (2015)
Modulation of microRNA editing, expression and processing by
ADAR2 deaminase in glioblastoma. Genome Biol., 16, 5.
Kawahara,Y., Zinshteyn,B., Chendrimada,T.P., Shiekhattar,R. and
Nishikura,K. (2007) RNA editing of the microRNA-151 precursor
blocks cleavage by the Dicer-TRBP complex. EMBO Rep., 8,
763–769.
Alon,S., Mor,E., Vigneault,F., Church,G.M., Locatelli,F.,
Galeano,F., Gallo,A., Shomron,N. and Eisenberg,E. (2012)
Systematic identiication of edited microRNAs in the human brain.
Genome Res., 22, 1533–1540.
Nevo-Caspi,Y., Amariglio,N., Rechavi,G. and Paret,G. (2011) A-to-I
RNA editing is induced upon hypoxia. Shock, 35, 585–589.
Borik,S., Simon,A.J., Nevo-Caspi,Y., Mishali,D., Amariglio,N.,
Rechavi,G. and Paret,G. (2011) Increased RNA editing in children
with cyanotic congenital heart disease. Intensive Care Med., 37,
1664–1671.
Ben-Zvi,M., Amariglio,N., Paret,G. and Nevo-Caspi,Y. (2013) F11R
expression upon hypoxia is regulated by RNA editing. PLoS One, 8,
e77702.
Camps,C., Saini,H.K., Mole,D.R., Choudhry,H., Reczko,M.,
Guerra-Assunção,J.A., Tian,Y.-M., Buffa,F.M., Harris,A.L.,
Hatzigeorgiou,A.G. et al. (2014) Integrated analysis of microRNA
and mRNA expression and association with HIF binding reveals the
complexity of microRNA expression regulation under hypoxia. Mol.
Cancer, 13, 1–21.
Alon,S. and Eisenberg,E. (2013) Identifying RNA editing sites in
miRNAs by deep sequencing. Methods Mol. Biol., 1038, 159–170.
Langmead,B., Trapnell,C., Pop,M. and Salzberg,S.L. (2009) Ultrafast
and memory-eficient alignment of short DNA sequences to the
human genome. Genome Biol., 10, R25.
Burroughs,A.M., Ando,Y., de Hoon,M.J.L., Tomaru,Y., Nishibu,T.,
Ukekawa,R., Funakoshi,T., Kurokawa,T., Suzuki,H., Hayashizaki,Y.
et al. (2010) A comprehensive survey of 3‘ animal miRNA
modiication events and a possible role for 3′ adenylation in
modulating miRNA targeting effectiveness. Genome Res., 20,
1398–1410.
Kozomara,A. and Grifiths-Jones,S. (2014) miRBase: annotating
high conidence microRNAs using deep sequencing data. Nucleic
Acids Res., 42, D68–D73.
Benjamini,Y. and Hochberg,Y. (1995) Controlling the false discovery
rate: a practical and powerful approach to multiple testing. J. R. Stat.
Soc. B, 57, 289–300.
Crooks,G.E., Hon,G., Chandonia,J.M. and Brenner,S.E. (2004)
WebLogo: A sequence logo generator. Genome Res., 14, 1188–1190.
John,B., Enright,A.J., Aravin,A., Tuschl,T., Sander,C. and
Marks,D.S. (2005) Correction: Human MicroRNA Targets. PLoS
Biol., 3, e264.
Agarwal,V., Bell,G.W., Nam,J.W. and Bartel,D.P. (2015) Predicting
effective microRNA target sites in mammalian mRNAs. Elife, 12,
e05005.
Kertesz,M., Iovino,N., Unnerstall,U., Gaul,U. and Segal,E. (2007)
The role of site accessibility in microRNA target recognition. Nat.
Genet., 39, 1278–1284.
Laganà,A., Forte,S., Russo,F., Giugno,R., Pulvirenti,A. and Ferro,A.
(2010) Prediction of human targets for viral-encoded microRNAs by