Abstract
Background
The studies of functions of circular RNAs (circRNAs) are heavily
focused on the regulation of gene expression through interactions with
multiple miRNAs. However, the number of predicted target genes is
typically overwhelming due to the synergistic complexity caused by two
factors ─ the binding of multiple miRNAs to a circRNA and the existence
of multiple targets for each miRNA. Analysis of common targets (ACT)
was designed to facilitate the identification of potential circRNA
targets.
Results
We demonstrated the feasibility of the proposed feature/measurement to
assess which genes are more likely to be regulated by circRNAs with
given sequences by calculating the level of co-regulation by multiple
miRNAs. The web service is made freely available at
[31]http://lab-x-omics.nchu.edu.tw/ACT_Server.
Conclusions
ACT allows users to identify potential circRNA-regulated genes and
their associated pathways for further investigation.
Electronic supplementary material
The online version of this article (10.1186/s12859-019-2966-3) contains
supplementary material, which is available to authorized users.
Keywords: Circular RNA, microRNA, Common targets, miRNA sponge
Background
Circular RNA (circRNA) is a newly recognized class of single stranded
regulatory RNA molecules with ends covalently closed through a
backsplice between a downstream splice donor and an upstream splice
acceptor. Recent discoveries through sequencing technology and
computational analyses have revealed the widespread existence of
circRNAs in animal cells and many other organisms [[32]1–[33]3].
CircRNAs contribute to transcriptional activation, post-transcriptional
modulation, translation, and protein interactions [[34]4–[35]8]. Among
these, the most popularly studied function of circRNA is that of a
miRNA sponge that regulates the gene expression network [[36]9–[37]11].
Pioneer studies have made great contributions dissecting and archiving
these relationships among miRNAs, circRNAs, and associated pathological
phenotypes [[38]12–[39]15]. However, studies investigating the
biological functions of circRNAs are largely limited to the scope of a
single miRNA linked to a single gene [[40]16–[41]18]. Thus, how to
identify a manageable gene list length and to consider its role as a
whole for further functional characterization has become a critical
task.
In this study, we developed and tested an intuitive concept that genes
targeted by more circRNA-associated miRNAs are more likely to be
modulated by a given circRNA. We established and provided a web service
for the analysis of common targets (ACT) for circRNAs to facilitate the
molecular characterization of the biological functions of various
circRNAs.
Implementation
The circRNA-associated miRNA-gene network typically involves many genes
targeted only once by a miRNA (Additional file [42]1: Figure S1, gray
nodes), and thus these genes may be less efficiently regulated by a
given circRNA. The central idea of ACT is to identify target genes with
high binding numbers for circRNA-associated miRNAs (Additional file
[43]1: Figure S1, blue nodes). To implement this analysis,
miRNA-binding sites in circRNAs were first extracted (Fig. [44]1a -
Step 1), followed by the identification of target genes for each
circRNA-associated miRNA (Fig. [45]1 - Step 2). Finally, the targeting
number for each gene was calculated (Fig. [46]1 - Step 3). The
flowchart for the sequent processes is summarized in Fig. [47]1b. The
ACT server takes circular RNA sequences in FASTA format. The default
miRNA sequences are downloaded from miRBase (Release 22) [[48]19,
[49]20]. First, the user-inputted sequence is extracted from the 5′ and
3′ ends (30 nt) and reverse-joined to produce a backsplice junction.
Then the list of miRNAs that potentially bind to the given circRNA is
generated by miRanda software (version 3.3a, Aug 2010) [[50]21] using
mature miRNA sequences from miRBase. In order to reduce the number of
predicted miRNA binding sites, the parameter ‘-strict’ is applied when
using miRanda. The position of miRNAs spanning the backsplice junction
is calibrated to the beginning of the original inputted sequence, and
the number of miRNA binding sites (nbs) is recorded for further
calculations. A list of the target genes for each miRNA with binding
site(s) on the given circRNA is generated using miRTarBase (Release
7.0) [[51]22]. The gene list is then collapsed for unique entries, and
for each gene (g), the number of targets for circRNA-associated miRNAs
(from the last step) is calculated as the sum of nbs (see Fig. [52]1a –
Step 3 for example). The genes in the list are ranked by the common
targeting time (CT).
[MATH: Common Targeting time of
GenegCTg=∑miRNAs∈g
nbs :MATH]
Fig. 1.
[53]Fig. 1
[54]Open in a new tab
Schematic illustration of ACT. a In step 1, miRNAs that bind to the
given circRNA were identified by the presence of binding sites. In
addition, the number of binding sites (nbs) for each miRNA was recorded
for further analysis in step 3. In step 2, the targets of each miRNA
were identified. It should be noted that some genes (colored in blue
and pink) were targets of multiple circRNA-associated miRNAs. In step
3, the nbs for miRNAs that bind to the same gene were summed and used
for further sorting. b The databases and tools integrated in ACT (see
the section on implementation)
Results
ACT-selected genes are enriched in specific biological pathways
ACT performs a distinct assessment compared to other metrics that
measure the binding energy or pairing score between miRNA and circRNA.
Compared to the density of miRNA binding sites, the binding energy and
pairing score given by miRanda for these predicted miRNAs on circRNAs,
CT for genes provides a more dynamic range to distinguish circRNAs
with/without identified miRNA sponge activity and a background dataset
(Additional file [55]1: Figure S2A-D and Table S1). The web interface
for ACT is neat and the output files are annotated with detailed
information (Fig. [56]2a and b). CircHIPK3, a previously identified
circRNA that targets multiple miRNAs [[57]10], was used as an example
(provided for users in the web interface, Fig. [58]2a). Raw analysis
using only the miRNA-target relationship revealed 7350 genes, and
approximately half of these genes were targeted by one or two miRNAs
(Fig. [59]3a and b; 3842 out of 7350, 52.27%). ACT is aimed at
identifying common targets that are targeted by several
circRNA-associated miRNAs. The top 100 genes were exported and are
listed in Fig. [60]3a. A few known circRNAs with and without known
sponge activity to multiple miRNAs were used for comparison.
Cytoplasmic circRNAs including circHIPK3, circCCDC66, circPVT1 and
circIRAK3 [[61]11, [62]23, [63]24], previously reported to function as
molecular sponges for multiple miRNAs, and nuclear circRNAs from FLI1
and UBR5 genes with distinct molecular functions other than as miRNA
sponges in the nuclei [[64]25, [65]26] were applied to the ACT
pipeline. To characterize whether the ACT-predicted circRNA-regulated
genes play biological roles, we adapted the concept of co-regulation or
the convergence of regulation. We assumed that the circRNA-targeted
genes are more likely to be conserved and involved in the same pathways
during evolution. The ACT-selected genes were subjected to pathway
enrichment analysis. The results of pathway enrichment analysis of the
genes selected by ACT from these cytoplasmic circRNAs with miRNA sponge
activity demonstrated that these genes tended to be enriched or
clustered in the same pathways (Fig. [66]3c). In sharp contrast, the
ACT-selected genes from two nuclear circRNAs (either top- or
bottom-ranked ones) showed no pathway enrichment (gene lists provided
in Additional file [67]1: Table S2). The lack of convergence in the
regulation of the pathways implied that the molecular functions of
these nuclear circRNAs were less likely to be as regulators than as
miRNA sponges.
Fig. 2.
Fig. 2
[68]Open in a new tab
Web interface for ACT. a The start page provides a simple and
straightforward interface for users to input the necessary information.
An example sequence and a link for an example of the analysis are
provided (circHIPK3). b The analysis example using circHIPK3 is
provided with detailed annotation
Fig. 3.
[69]Fig. 3
[70]Open in a new tab
ACT-selected genes identified important biological pathways. a The
exported ACT results for circHIPK3. The top 100 genes ranked by their
common targeting times (parenthesized) are shown at the bottom. b The
distribution of the common targeting times of circHIPK3-regulated
candidate genes is shown as a pie chart. c The ACT-prioritized genes
(top 100) and low ranked genes (bottom) from circHIPK3, circCCDC66,
circPVT1 and circIRAK3 were subjected to pathway enrichment analysis
ACT enables the distinguishment of circRNAs with or without potential miRNA
sponge activity
To further elucidate the performance and potential application of ACT,
we evaluated the convergence of pathway regulation among different
metric-derived gene lists. The target genes of the top 10% of miRNAs
according to the pairing score or binding energy in the given circRNA
sequence were subjected to pathway enrichment analysis. While
enrichment analyses from the gene lists derived from the ranked energy
or scores failed to distinguish circRNAs with/without sponge activity
from multiple miRNAs (Fig. [71]4, left and central panels), pathway
analyses with ACT-selected gene lists showed significantly more
convergent pathways (Fig. [72]4, right panel). This implied that the
genes targeted multiple times by circRNA-associated miRNAs tend to be
more biologically significant. ACT is a novel tool to dissect the
molecular and cellular functions of circRNAs. Compared to other pioneer
databases for annotating circRNA/miRNA interactions (Additional file
[73]1: Table S3) [[74]13, [75]14, [76]27], ACT not only provides a list
of interacting miRNAs, but also a ranked gene list in a manageable
length ready for further functional and/or experimental
characterization.
Fig. 4.
[77]Fig. 4
[78]Open in a new tab
Performance assessment of ACT and alternative miRNA-related metrics The
gene lists derived from different ranked metrics (binding energy,
pairing score, and ACT) were subjected to pathway enrichment analysis.
Each bar represents a circRNA or random transcript according to the
label on the x axis. The top row shows the number of pathways while the
bottom row shows the p-value estimated through a permutation test
drawing from the full target gene list of circRNA-associated miRNAs.
sponge: circRNAs with known sponge activity to multiple miRNAs.
-sponge: nuclear circRNA without known miRNA sponge activity. circRNAs:
a group of circRNA with unknown molecular function. Transcripts: random
transcript as background dataset
Conclusion
Taken together, analysis using ACT-selected genes provided a novel and
intuitive method to differentiate the molecular and biological
functions of circRNAs. Incorporating the concept of co-regulation by
multiple circRNA-associated miRNAs provides a straightforward method
for assessing the potential targets of circRNAs and will help
prioritize the candidates as well as identify major pathways for the
functional study of circRNAs.
Availability and requirements
Project name: ACT
Project home page: [79]http://lab-x-omics.nchu.edu.tw/ ACT_Server/
Operating system(s): Platform independent (Web-based service)
Programming language: Perl 5 and R 3.4.3
Other requirements: N/A
License: GNU GPL; non-academic user: license needed
Additional file
[80]Additional file 1:^ (270.2KB, pdf)
Figure S1. A schematic illustration of miRNA-gene interaction. Figure
S2. Metrics comparison for circRNA-associated miRNAs. Table S1. The
miRNA-related metrics for circRNA. Table S2. Gene lists from ACT for
pathway analysis. Table S3. The comparison of platforms/tools for
circRNA–miRNA–gene network. (PDF 270 kb)
Acknowledgements