Identification of miRNAs during mouse postnatal ovarian development and superovulation
Khan et al. Journal of Ovarian Research
Identification of miRNAs during mouse postnatal ovarian development and superovulation
Hamid Ali Khan 0
Yi Zhao 0
Li Wang 0
Qian Li 0
Yu-Ai Du 0
Yi Dan 0
Li-Jun Huo 0
0 Key Laboratory of Agricultural Animal Genetics , Breeding and Reproduction , Education Ministry of China, College of Animal Science and Technology, Huazhong Agricultural University , Wuhan 430070 , People's Republic of China
Background: MicroRNAs are small noncoding RNAs that play critical roles in regulation of gene expression in wide array of tissues including the ovary through sequence complementarity at post-transcriptional level. Tight regulation of multitude of genes involved in ovarian development and folliculogenesis could be regulated at transcription level by these miRNAs. Therefore, tissue specific miRNAs identification is considered a key step towards understanding the role of miRNAs in biological processes. Methods: To investigate the role of microRNAs during ovarian development and folliculogenesis we sequenced eight different libraries using Illumina deep sequencing technology. Different developmental stages were selected to explore miRNAs expression pattern at different stages of gonadal maturation with/without treatment of PMSG/hCG for superovulation. Results: From massive sequencing reads, clean reads of 16-26 bp were selected for further analysis of differential expression analysis and novel microRNA annotation. Expression analysis of all miRNAs at different developmental stages showed that some miRNAs were present ubiquitously while others were differentially expressed at different stages. Among differentially expressed miRNAs we reported 61 miRNAs with a fold change of more than 2 at different developmental stages among all libraries. Among the up-regulated miRNAs, mmu-mir-1298 had the highest fold change with 4.025 while mmu-mir-150 was down-regulated more than 3 fold. Furthermore, we found 2659 target genes for 20 differentially expressed microRNAs using seven different target predictions programs (DIANA-mT, miRanda, miRDB, miRWalk, RNAhybrid, PICTAR5, TargetScan). Analysis of the predicted targets showed certain ovary specific genes targeted by single or multiple microRNAs. Furthermore, pathway annotation and Gene ontology showed involvement of these microRNAs in basic cellular process. Conclusions: These results suggest the presence of different miRNAs at different stages of ovarian development and superovulation. Potential role of these microRNAs was elucidated using bioinformatics tools in regulation of different pathways, biological functions and cellular components underlying ovarian development and superovulation. These results provide a framework for extended analysis of miRNAs and their roles during ovarian development and superovulation. Furthermore, this study provides a base for characterization of individual miRNAs to discover their role in ovarian development and female fertility.
Non-coding RNAs; Deep sequencing; Ovarian development; Mouse
Ovarian folliculogenesis is a complex biological process,
which is tightly regulated by the coordination of large
number of genes . In animals, developmental process
starts with oogenesis when RNA and protein are
combined resulting in the growth of oocyte. In addition,
oocyte development is also regulated by complex genetic
network especially transcription regulators . The
extent of transcription reflects the importance of
messenger RNA (mRNA) during the growth of oocytes, hence
early development of oocyte is exclusively dependent on
the maternally inherited components, including proteins
and RNAs . So far, advanced technology led to the
discovery of some non-coding RNAs like small nucleolar
RNAs, small interfering RNAs, microRNAs and
antisense RNAs, thus suggesting that eukaryotic
transcriptome is much more complex than expected .
MicroRNAs (miRNAs) belongs to small non-coding
RNAs which are of prime importance due to their roles
in regulating genes and genomes at different levels such
as chromatin structure, chromosome segregation,
transcription and RNA processing . Likewise mRNA,
microRNA expression shows vibrant changes during the
development process as extensive number of genes
involved in the process of oogenesis, are influenced by
miRNAs are miniature (typically ~22 nucleotides in
length) non-coding RNAs that play significant roles in
post-transcriptional regulation of specific mRNAs. Most
miRNAs arise from very long transcripts known as
primary miRNA (pri-miRNA) by drosha and its cofactor
DGCR8 (DiGeorge syndrome critical region gene 8) in
nucleus converting it to ~70-100 bp precursor miRNA
(pre-miRNA). After the transport of pre-miRNAs from
nucleus to cytosol, Dicer (a RNA III endonuclease)
process precursor miRNA by removing hairpin loop thus
converting it to mature miRNA . Previous studies
suggested that conditional knockout of Dicer in the
ovary leads to sterility; thus providing strong evidence of
miRNAs involvement in ovarian development .
Furthermore, Amhr2-Cre mediated deletion of Dicer in
mice resulted in reduced ovarian function due to loss of
miRNAs [9–11]. Dicer1 conditional knockout (cKO)
mice shows accelerated early follicles recruitment and
more degenerate follicles in ovaries. Furthermore,
significant differences were noted in some follicle
development related genes suggesting that miRNA expression is
time and gene dependent .
miRNA and mRNA interactions through direct
basepairing causes suppression of translation or assist mRNA
degradation in sequence specific manner [13, 14]. By this
way miRNAs influence various cellular processes e.g.,
development, cell proliferation and differentiation,
selfrenewal and apoptosis etc. . Also, the mechanism of
miRNA mediated gene regulation is quite complex, as a
single miRNA can target thousands of genes transcripts
and vice versa . Recent studies have shown that
certain reproductive processes are strictly regulated at the
transcriptional and post-transcriptional levels .
Along with, a novel mechanism of miRNA mediated
post-transcriptional regulation has revealed lately which
is regarded as an important regulator of reproductive
processes [17, 18].
Folliculogenesis is a complex process involving series
of morphological and functional changes depending on
the type of cells and developmental stage . Previous
investigations have evaluated miRNA transcriptomes
from the reproductive organs in different organisms to
decipher their expression profile and have shown their
roles in pathology, fertility and development of ovary
[14, 16, 20–22]. Although these findings provide
valuable information about individual miRNAs differentially
expressed in specific type of ovarian cells with/without
response to gonadotropic hormones, the number of
experimentally validated miRNAs expressed in the ovary is
still very limited. For example, miR-132 and miR-212
respond to luteinizing hormone (LH)/human chorionic
gonadotropin (hCG) thus, these miRNAs play important
roles in post-transcriptional regulation of granulosa cells
. Similarly, miR-224, miR-21 and miR-145 regulate
proliferation and apoptosis of granulosa cells [24–26].
Prior cloning and sequencing techniques identified
different number of miRNAs at specific stage of ovarian
development. For example, Ro et al. identified 122 miRNAs from
adult mice ovary while 516 miRNAs were identified from
new born mice ovary by Ahn et al. [23, 27]. Mishima et al.
and Tripurani et al. reported expression of 154 miRNAs and
58 miRNAs in adult mice ovary and bovine fetal ovary,
respectively [1, 28]. However, these studies provide limited
information about involvement of miRNAs in postnatal
development. Therefore, identifying the expression pattern
of miRNAs in mouse ovary at different stages of ovarian
development became the key step to discover their roles in
ovarian development and folliculogenesis.
To date, number of experimentally validated miRNAs
playing vital roles in ovarian development is quite
insufficient. Thus, the exceptional volume of sequence data
generated from our work provided distinctive
opportunity to mine for differentially expressed as well as novel
miRNAs that have evaded previous cloning and
sequencing techniques. This data is in line with expression
pattern of experimentally validated miRNAs implying the
authenticity of the differentially expressed miRNAs in
this study. Furthermore, we investigated potential novel
miRNAs along with differentially expressed miRNAs and
predicted their roles in various pathways and Gene
ontologies (GOs). Moreover, this study provided important
information about the miRNAs expression pattern
during postnatal development and superovulation in
female mice. This further provides baseline for
experimental validation of these differentially expressed and
potential novel miRNAs to reveal their respective roles
and regulatory mechanism during postnatal
development and ovulation process at the molecular level.
Kunming female mice were obtained from the Centre of
Laboratory Animals of Hubei Province (Wuhan, PR
China). Mice were housed under controlled temperature
(20 °C −24 °C) and lighting (12 h light/12 h darkness)
with food and water ad libitum. All animal treatment
procedures were approved by the Ethical Committee of
the Hubei Research Center of Experimental Animals
(Approval ID: SCXK (Hubei) 2008–0005).
Primordial follicle activation are known to occur and
begin to develop in the ovary of 3 days old female mice,
and 21 days old female mice at stage of puberty begin to
ovulate for the first time. Furthermore, in the
preliminary experiment, we found that most follicles in 6 days
old, 8 days old, 12 days old and 15 days old mice ovaries
are primary follicles, secondary follicles with 2–3 layers
of granulosa cells, and secondary follicles with multiple
layers of granulosa cells, respectively. Therefore, we
obtained ovaries from 6 days old (6d), 8 days old (8d),
12 days old (12d), 15 days old (15d) and 21 days old
(21d) of Kunming white female mouse for analysis of
microRNAs expression profile during postnatal
development and follicular development after primordial follicle
activation. For analysis of microRNAs expression during
ovulation, 21d old mice were injected with 10 IU of
pregnant mare serum gonadotropin (PMSG) for 48 h
and then with 10 IU of human chorionic gonadotropin
(hCG). Mice were scarified by cervical dislocation and
ovaries were collected at 6 h and 48 h after PMSG and
6 h after hCG treatment and RNA was extracted for
deep sequencing of miRNAs expression profile to reveal
the response of miRNAs to PMSG/hCG and during
super-ovulation. Therefore, the ovary samples were
marked as 6d, 8d, 12d, 15d, 21d, P6 (PMSG 6 h), P48
(PMSG 48 h), and h6 (PMSG 48 h and hCG 6 h). For
each library preparation, total RNA was pooled isolated
from ovaries of at least 10 female mice.
Small RNA library construction and deep sequencing
Total RNA was extracted from ovaries using Trizol reagent
(Invitrogen, Carlsbad, CA, USA) following manufacturer
protocol and RNA quality was analyzed by using nanodrop
ND-8000 spectrophotometer (Thermo Electron Corporation,
USA) at 260/280 nm. From each sample, 2 μg of total RNA
was used for deep sequencing using Hiseq 2000 sequencing
platform from illumina (Illumina, San Diego, CA, USA) at
Genergy Biotechnology Co., Ltd., Shanghai, China). Briefly, 16
to 26 nt small RNA fraction was purified from total RNA and
enriched from denaturating polyacrylamide gel electrophoresis
(PAGE). Adapters were ligated at 3’ and 5’ ends using T4 ligase
and further small RNA was subjected to RT-PCR for
amplification (12 Cycles). PCR product was further purified using
polyacrylamide TBE (Tris/Borate/EDTA) gel and used for
sequencing. Sequencing files were extracted from image file
generated by Illumina genome analyzer.
Bioinformatics analysis and statistics
After filtering out adapters sequences and low quality
reads, clean reads were mapped to UCSC mouse
genome mm9 (http://genome.ucsc.edu/) using NCBI Mega
BLAST. Moreover Rfam version 10.1 (http://rfam.sanger.
ac.uk/) was used for removal of other non-coding RNAs.
Remaining sequences were analyzed for miRNAs using
BLAST search against miRNA database (miRBase V.20,
www.mirbase.org) to identify conserved microRNAs in
mouse (Mus musculus). Perfectly matched sequences were
regarded as conserved sequences.
Differential expression analysis
To analyze differentially expressed microRNAs from all
eight libraries (6d, 8d, 12d, 15d, 21d, P6, P48 and h6),
we used the criteria as reported by others. Briefly,
miRNA expression was normalized to get the expression
of transcript per million by using the formula.
Normalized expression = (Actual miRNA sequencing reads
count/Total clean reads count) × 1,000,000. After
normalization, the expression values of non-detected
miRNAs were revised to 0.01. miRNAs whose
normalized expression value was <1 in both samples [e.g., in
case of 6d-8d, 6d is (sample 1) while 8d (sample 2)] were
excluded from the following differential expression
analysis due to low expression. Statistical significance of
miRNA expression in each group was calculated using
Bioconductor R package [29–33].
Quantitative RT- PCR (qRT-PCR)
To validate the differentially expressed miRNAs identified
using deep sequencing technology, eight miRNAs were
further selected and their relative expression levels were
analyzed in different sized follicles (i.e., 100 μm −130 μm,
200 μm -280 μm, 450 μm -550 μm, 500 μm -600 μm
isolated from ovary samples of 12d, 21d, P48, and h6, the
same as in sequencing samples respectively). miRNA was
extracted using miRcute miRNA Isolation Kit (Tiangen,
Beijing China) according to manufacturer protocol. cDNA
was synthesized using miScript II RT Kit (QIAGEN) and
qRT-PCR was performed using the miScript SYBR Green
PCR Kit (QIAGEN) according to the manufacturer’s
protocol. The reaction mixtures were incubated in a
96-well plate at 95 °C for 15 min followed by 40 cycles of
94 °C for 15 s, 60 °C for 30 s and 70 °C for 30 s. All
reactions were run in triplicate. The primers for miRNAs
have the same sequences as Mus Muscullus miRNAs
with an appropriate adjustment at their 5’ terminus.
Expression of target miRNA in each sample was
normalized to the small nuclear gene U6. Relative miRNA levels
were calculated using the comparative threshold 2−ΔΔCt
RNA-seq data is presented as means ± standard deviations
(SD). Differences between samples were regarded as
significant at p < 0.01. Furthermore, each miRNA expression level
is presented as 2−ΔΔCt means ± SE (standard error), and
error bars indicate the standard error of 2−ΔΔCt mean values.
To examine the significance of differential expression level
in each miRNA between different size follicles One-way
ANOVA and Duncan’s Multiple Range test were used by
using SPSS (Version17.0; SPSS, Chicago, IL, USA). The
difference was considered as significant when P <0.05.
Sequence analysis of small RNAs in mouse ovary
To investigate miRNAs involved in the postnatal
development and ovulation of mouse ovary, eight small RNA
libraries were constructed by Illumina Hiseq 2000 small
RNA deep sequencing technology. Raw reads were
processed by filtering out low quality sequences, empty
adapters and single read sequences. Clean reads of 16–
26 nt (Fig. 1) were selected for further analysis from
mice postnatal development and superovulated
sequenced libraries, respectively (Table 1).
Differentially expressed miRNAs during postnatal
development and superovulation in mouse ovaries
The main purpose of this study was to identify miRNAs
involved in mouse ovarian development and
folliculogenesis. According to the changes in relative miRNA
expression among eight libraries representing postnatal
developmental and superovulated ovaries, in total 58, 73,
64, 31, 24, 21 miRNAs were differentially expressed
during 6d-8d, 8d-12d, 12d-15d, 15d-21d, 21d-P6, P48-h6
respectively (|log2Ratio| ≥ 1, P-value ≤ 0.01). Further
analysis showed that among all differentially expressed
miRNAs, 61 miRNAs showed more than two fold
differences in terms of expression. Among the up-regulated
miRNAs, mmu-mir-1298 had the highest fold change
with 4.025 during 21d-P6 followed by mmu-mir-212 and
mmu-mir-132 with a fold change of 3.71 and 3.28,
respectively. Among down regulated miRNAs,
mmu-mir150 was down-regulated more than 3 fold during
12d15d (Fig. 2).
qRT-PCR analysis of miRNAs expression in ovarian follicles
To further validate these differentially expressed miRNAs
identified from the mouse ovary, the expression levels of
miR-199a, miR-470, miR-871, miR-34c let-7a, miR-7a,
miR-351, miR-191 were further examined in different
size follicles (i.e., 100 μm −130 μm, 200 μm -280 μm,
450 μm -550 μm, 500 μm -600 μm) using qRT-PCR
assay. qRT-PCR results showed that some of the miRNAs
exhibit developmental stage-specific expression patterns
in ovarian development. The expression patterns of
miR199a, miR-470, miR-871, shows relatively higher
expression in small preantral follicles as compared to large
Fig. 1 Length distribution and abundance of miRNA sequences in mouse ovary by Illumina deep sequencing. Sequence length distribution of
clean reads based on the abundance and distinct sequences; the most abundant size class was 22 nt, followed by 21 nt and 23 nt. a) 6d, b) 8d,
c) 12d, d) 15d, e) 21d, f) P6 g) P48, h) h6
Sample Clean Data Reads Reads failed
Id (Read Num) (>=1 alignment) to align
The source is UCSC genome database mm9. Clean data refers to removal of
adopters and low quality reads
antral follicles with the similar expression pattern as the
results of deep sequencing. However, the expression
dynamics of miR-34c, let-7a, miR-7a were different; the
expression level was increased with increase in size of
follicles. The results indicate that the expression pattern
of some microRNAs are consistent with our deep
sequencing results (Fig. 3), but others not. Further
QRTPCR assay is needed to validate the expression pattern of
selected microRNAs obtained from deep sequencing
Sequencing data was subjected to Rfam to filter out
rRNAs, tRNAs, snRNAs and snoRNAs. The processed
data was used for novel microRNA identification by
miRDeep2, an algorithm based on microRNA biogenesis.
miRDeep2 predicted 160 potential novel miRNAs at the
relatively stringent score cut-off of 5 and signal-to-noise
ratio of 12.1 (Additional file 1: Table S1). For each set of
newly identified miRNA, we used a variety of assessment
methods to evaluate the predictive accuracy. We used
both, False Positive rate (FPR) and True Positive Rate
(TPR) for assessment of predicted results. Furthermore,
RNA-fold was used to confirm the structure of predicted
miRNAs . After filtering out the predicted novel
miRNAs by removal of loci matching other RNA genes,
keeping only novel miRNAs with significant rand fold
p-value (<0.05), with miRDeep2 score >5, and analyzing
the hairpin structure of the microRNAs, the list was
reduced to 10 potential novel microRNAs (Additional file 2:
Table S2). For detection of miRNAs in deep sequencing
data by miRDeep2, a score cutoff equivalent to a prediction
signal-to-noise ratio of 10 is most often used .
Putative target genes of differentially expressed
miRNA mediated gene expression regulation plays
significant role in development, maturation and ovulation
by governing self-renewal, proliferation, differentiation
and apoptosis [37, 38]. To figure out miRNA putative
target genes associated with maturation and
superovulation of ovary at different stages of development,
miRanda public database was used. Target genes of
differentially expressed miRNAs were predicted
according to previously established criteria [39–42]. For
rigorous screening of highly credible miRNA target genes,
three basic criteria were used 1) Conservation, 2)
Energy, 3) mirSVR score. On the basis of these criteria we
selected 71 differentially expressed microRNAs targeting
3324 putative target genes (Data not shown). Additionally,
we extended our approach and selected 20 microRNAs
from these 71 differentially expressed microRNAs for
further validation using seven different target prediction
programs (DIANA-mT, miRanda, miRDB, miRWalk,
RNAhybrid, PICTAR5, TargetScan) to enhance the credibility of
the target genes . Genes targeted by five or more
different programs are shown in Additional file 3: Table S3.
Moreover, among these putative target genes we
further identified ovary specific genes. These genes play
vital role in development of ovary, folliculogenesis,
ovulation and thus influencing female fertility. Changes in
miRNA expression pattern regulate these potential
target genes hence suggesting a collaborative role between
microRNAs and mRNAs during ovarian development.
Some of these genes are targeted by multiple
microRNAs while in other cases multiple genes are targeted
by a single miRNA as shown in Fig. 4.
Gene ontology and pathway annotation
For further understanding the roles of differentially
expressed miRNAs in physiological functions and biological
processes during postnatal development and ovulation,
target prediction was performed by using public database
(miRanda). Human Gene Ontology (GO) database and
Koyoto Encyclopedia of Genes and Genomes (KEGG)
database were used for GO annotation and KEGG pathway
analysis to identify functional modules regulated by these
miRNAs. The GO annotation enrichment results showed
that regulation of transcription and regulation of RNA
metabolic process were significantly enriched during all the
six differentially expressed libraries except P48-h6 in terms
of biological function. While regulation of transcription
Fig. 2 Heatmap showing differentially expressed miRNAs. Heat map
shows differentially expressed miRNAs among six libraries with a
log2Ratio ≥ 1. Bright red and light yellow represent down-regulated
and up-regulated miRNAs respectively. a) 6d-8d, b) 8d-12d,
c) 12d-15d, d) 15d-21d, e) 21d-P6, f) P48-h6
and phosphate metabolic process were among highly
enriched biological functions based on number of genes
involved during P48-h6 (Additional file 4: Figure S1).
Similarly, plasma membrane part and ion binding were
among the most significant cellular components and
molecular functions in terms of GO annotation (Additional
file 5: Figure S2 and Additional file 6: Figure S3). Moreover,
KEGG and Biocarta pathway analysis revealed that
Pathway in cancer, MAPK signaling pathway,
Wntsignaling pathway and oocyte meiosis ranked among
the most enriched pathways (Fig. 5). Although the
false-positive predictions always exist, we suggest that
these targets have high possibility of being regulated by
miRNAs which are involved in the development of mouse
ovary and ovulation.
The discovery of miRNAs revolutionized the
unanticipated regulation of transcriptome and proteomes.
Illumina deep sequencing transformed discovery of
miRNAs as this technique is considered an efficient way
for miRNA discovery and is widely used to produce
small RNA profiles in various organisms. Although some
miRNAs have been proved critically involved in the
regulation of ovarian granulosa cells by using real time
PCR and other techniques, granulosa cells are only one
type of cells in follicles while follicles grow inside the
ovary and ovary grows as a whole organ during postnatal
development. Furthermore, due to the complexity of
ovarian development and folliculogenesis, the study of
single or multiple miRNAs only in granulosa cells might
have some limits, which could not reflect the changes in
profile of miRNAs and the regulation of target genes
involved in ovarian development and folliculogenesis.
Herein, detailed miRNA profiles of mice ovaries at 6d,
8d, 12d, 15d, 21d, P6, P48 and h6 using Illumina deep
sequencing technique were obtained in this study. These
results reported the miRNA expression profiles at
different time points of postnatal development and
superovulation from mice ovaries, which at least partially
represent the different stages of folliculogenesis.
Furthermore, the differentially expressed miRNAs and their
target genes were also revealed between the near groups,
which could efficiently reflect the dynamic changes of
miRNAs during ovarian development and
folliculogenesis. The gene ontology and pathway annotation of
target genes of those differentially expressed miRNAs
were further analyzed to reveal the dynamic changes of
Fig. 3 qRT-PCR validation of eight miRNAs in different sized follicle. qRT–PCR of selected known miRNAs in different size follicles (100 μm −130 μm,
200 μm -280 μm, 450 μm -550 μm, 500 μm -600 μm). miRNA was isolated and cDNA was synthesized from different size follicles followed by qRT-PCR.
Capital letters shows expression profile of respective miRNA in deep sequencing data while small letters represents their expression profile in
different size follicles using qRT-PCR. A) Expression profile of mmu-mir-199a in sequencing data. a) Expression profile of miR-199a through
qRT-PCR. B) Expression profile of mmu-mir-470 in sequencing data. b) Expression profile of miR-470 through qRT-PCR. C) Expression profile of
mmu-mir-871in sequencing data. c) Expression profile of miR-871 through qRT-PCR. D) Expression profile of mmu-mir-351 in sequencing data.
d) Expression profile of miR-351 through qRT-PCR. E) Expression profile of mmu-mir-191 in sequencing data. e) Expression profile of miR-191
through qRT-PCR. F) Expression profile of mmu-mir-34c in sequencing data. f) Expression profile of miR-34c through qRT-PCR. G) Expression
profile of mmu-let-7a in sequencing data. g) Expression profile of let-7a through qRT-PCR. H) Expression profile of mmu-mir-7a in sequencing
data. h) Expression profile of miR-7a through qRT-PCR
biological and cellular processes inside of the ovary
during postnatal development and ovulation. We suggest
that present work provides important information for
understanding the biological and cellular processes and
regulation of miRNA and target genes in the whole
ovary during postnatal maturation and folliculogenesis.
In present study, the sequencing analysis showed that
the dominant size of small RNAs in mice ovary was
22 nt followed by 21 and 23 nt sequences (Fig. 1). These
results resemble to typical Dicer-processed small RNA
products with known 19–24 nt range for miRNAs. Our
sequencing data is consistent with previous findings in
mice  and pig , but vary from Holstein Cattle
ovary where the 20 nt size was the most abundant,
followed by 22 nt . Another study in bovine ovary
indicated that 21 nt is the predominant size ,
possibly because of difference in species.
In liberaries from postnatal developmental and
superovulated mice ovaries, let-7 miRNA family was abundantly
cluster with let-7a being the most abundantly expressed
miRNA. Previous finding also showed abundant expression
of let-7 miRNA family in the ovary and oocyte of bovines
[1, 45, 46], as well as in murine ovaries and testis . Thus,
relative abundance suggests that members of let-7 family
have important roles in cell fate determination and
associated with regulating housekeeping genes during ovarian
development . Furthermore, mmu-mir-101,
mmu-mir148a, mmu-mir-26a, and mmu-mir-30d were profuse in our
sequencing libraries, as already reported in other animal
gonads [1, 28, 37].
Likewise, mmu-mir-21, mmu-mir-125b,
mmu-mir16b, mmu-mir-143 and mmu-mir-199a-3p were
expressed abundantly in all libraries despite of changes
in expression with development thus suggesting its role
in basic reproductive activities. These miRNAs were also
reported previously to be among the most prevalent
miRNAs in whole ovaries of mice, cattle and pigs
[28, 37, 44–46, 49]. Others predominantly expressed
miRNAs e.g., mmu-mir-125b, mmu-mir-199a-3p,
mmumir-29a and mmu-mir-15b targets several ovarian genes
Fig. 4 Ovary specific genes targeted by microRNAs. Network shows microRNAs and their predicted target genes. Ovary specific genes are
highlighted in the network. Green rectangles represent microRNAs while blue circles represent target genes. The specific interaction is
highlighted by red lines between microRNAs and ovary specific target genes
and involved in several biological functions like cell
signaling, cell death, cell cycle regulation, cellular growth
and differentiation and endocrine system . During
superovulation, mmu-mir-351, mmu-mir-30c,
mmumiR-26a, mmu-mir-25 expressed extensively as already
reported by Fiedler et al. using microarray technology
. High expression of mmu-mir-322 shows its
involvement in cell differentiation, folliculogenesis and
overall ovarian development . Therefore, these
miRNAs and their target genes are greatly associated
with basic ovarian functions and cellular processes.
Previous studies reported that up-regulation of miR-21
in murine granulosa cells pre and post hCG/LH surge
arresting apoptosis in preovulatory granulosa cells. In
addition, increased apoptosis and reduced ovulation rate
was observed in granulosa cells with knockdown of
miR21 [25, 50]. In current study, differential expression
of mmu-mir-21 exhibited significant fold change i.e.,
Fig. 5 Pathway annotation. Pathway annotation of differentially expressed miRNAs based on predicted target genes involved in different
pathways during 6d-8d (a), 8d-12d (b), 12d-15d (c), 15d-21d (d), 21d-P6 (e), P48-h6 (f). Vertical axis shows pathways while horizontal axis shows
number of genes involved in respective pathway
1.34-fold during 21d-P6, even more significant response
to hCG, suggesting that previous findings are in
concordance with our deep sequencing results. Likewise,
Guijun et al. reported that miR-145 suppressed mouse
granulosa cells proliferation by targeting ACR1B via
activin induced SMAD2 phosphorylation . Differential
analysis of mmu-mir-145 showed down-regulation with
ovarian growth i.e., log2 fold change was 1.53 during
6d8d and −1.12 during 12d-15d thus showing its roles in
miRanda algorithm showed that, activin receptor 1
(ACVR1) is predicted target gene for mmu-mir-193,
mmu-mir-294, mmu-mir-295 and mmu-mir132. ACVR1
mRNA is present in granulosa-luteal cells and cumulus
oocyte complexes during in vitro maturation which play
roles in follicular development and steroid metabolism
[52, 53]. Bioinformatics analysis showed that
mmu-mir470 targets TGIF1 (TGFB-induced factor homeobox
1) while mmu-mir-300 and mmu-mir-880 targets ZEB2
(zinc finger E-box binding homeobox 2), showing
participation in the regulation of TGF-β signaling . As
TGF-β signaling is essential for folliculogenesis and
oogenesis in mammalian ovaries , hence implied the
indirect involvement of these miRNAs in folliculogenesis
and oogenesis. Furthermore, miR-124 is reported to be
actively involved in the suppression of SOX9 which is
testis development gene, to inhibit production of SOX9
protein in ovary .
Experimental validation of miRNA targets is a
challenging approach which ultimately led to the use of in silico
approaches to predict miRNAs targets . Until now,
many algorithms have been designed based on different
pairing approaches between miRNA and mRNA . In
current study, we used miRanda algorithm for target
gene prediction which was initially designed for the fruit
fly and then extended to other organisms including
mouse. miRanda algorithm is mainly based on energy
involved between miRNA:mRNA physical interaction .
To further ascertain the miRNA target interaction we
used seven different target prediction programs for
differentially expressed microRNAs. We identified many
putative genes targeted by differentially expressed
miRNAs involved in the postnatal maturation and
ovulation in mouse. Some of these predicted target genes play
key roles in gonadal maturation and ovulation (Fig. 4).
For example, TGF-β superfamily members are involved
granulosa cell proliferation, estrogens, and progesterone
production . Inhibin and activin play significant roles
in follicular development and differentiation .
Receptors for BMPs (Bone morphogenetic proteins) are
present in ovaries, thus play role in differentiation of
granulosa cells .
Due to challenges in experimental validation of
miRNAs targets, in silico tools are better approach for
target prediction based on different base pairing
properties between miRNA and mRNA . The better
approach is to use several target prediction tools and due
to this reason we used this approach for some
differentially expressed microRNAs. Taken together, our findings
and other evidences support that these differentially
expressed miRNAs play key role in ovarian development
and fertility. Analyzed target genes shows involvement
in broad range of signaling cascades and pathways of the
The above findings as well as our qRT-PCR results of
individual miRNAs are consistent with our deep
sequencing data implying high significance of our data and
suggesting the critical roles of these differentially expressed
miRNAs not experimentally validated so far in ovarian
development and folliculogenesis. Further studies will be
needed to validate the biological significance of these
differentially expressed and novel miRNAs identified in
present work, to reveal its specific roles and regulatory
mechanism in specific cells of ovary during postnatal
development and ovulation.
This study explored and evaluated microRNA
transcriptome in mouse postnatal ovarian development and
superovulation at different stages, thus provided valuable
information about the dynamic changes of miRNAs
profile during ovarian development. Results shows that
some of microRNAs either up- or down-regulated
during specific period thus indicating their role at a specific
stage of ovarian development. Moreover, predicted target
genes showed involvement in different pathways and
GO terms. Along with, we also reported 10 novel
miRNAs that evaded previous sequencing techniques.
Further functional characterization of these differentially
expressed and novel microRNAs at specific stage of
ovarian development will help to elucidate their specific
role in follicle growth, ovarian development as well as
ovulation. The information we provided in present study
will help to identify candidate miRNAs targeting specific
molecular and cellular pathways important for follicular
development, ovulation as well as ovarian dysfunction.
Additional file 2: Table S2. List of putative novel microRNAs.
Additional file 3: Table S3. List of putative target genes.
Additional file 4: Figure S1. Partial GO enrichment in biological
process. The figure shows partial gene enrichment for differentially
expressed miRNAs in terms of biological process during 6d-8d (a), 8d-12d
(b), 12d-15d (c), 15d-21d (d), 21d-P6 (e), P48-h6 (f). Vertical axis shows GO
terms while horizontal axis shows number of genes involved.
Additional file 5: Figure S2. Partial GO enrichment in cellular
components. The figure shows partial gene enrichment for differentially
expressed miRNAs in terms of cellular components during 6d-8d (a),
8d-12d (b), 12d-15d (c), 15d-21d (d), 21d-P6 (e), P48-h6 (f). Vertical axis
shows GO terms while horizontal axis shows number of genes involved.
Additional file 6: Figure S3. Partial GO enrichment in molecular
functions. The figure shows partial gene enrichment for differentially
expressed miRNAs in terms of molecular function during 6d-8d (a),
8d-12d (b), 12d-15d (c), 15d-21d (d), 21d-P6 (e), P48-h6 (f). Vertical axis
shows GO terms while horizontal axis shows number of genes involved.
This study was supported by National Natural Science Foundation of China
(Grant No.31171273) and the Fundamental Research Funds for the Central
Universities (Program NO. 2014PY045). The experimental work was
conducted in the Animal Genetic Breeding and Reproduction Laboratory of
Huazhong Agricultural University, Hubei, China.
1. Tripurani SK , Xiao C , Salem M , Yao J. Cloning and analysis of fetal ovary microRNAs in cattle . Anim Reprod Sci . 2010 ; 120 ( 1-4 ): 16 - 22 .
2. Song JL , Wessel GM . How to make an egg: transcriptional regulation in oocytes . Differ Res Biol Divers . 2005 ; 73 ( 1 ): 1 - 17 .
3. Wassarman PM , Kinloch RA . Gene expression during oogenesis in mice . Mutat Res . 1992 ; 296 ( 1-2 ): 3 - 15 .
4. Kusenda B , Mraz M , Mayer J , Pospisilova S. MicroRNA biogenesis, functionality and cancer relevance . Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub . 2006 ; 150 ( 2 ): 205 - 15 .
5. Carthew RW , Sontheimer EJ. Origins and Mechanisms of miRNAs and siRNAs. Cell . 2009 ; 136 ( 4 ): 642 - 55 .
6. Tang F , Kaneda M , O'Carroll D , Hajkova P , Barton SC , Sun YA , et al. Maternal microRNAs are essential for mouse zygotic development . Genes Dev . 2007 ; 21 ( 6 ): 644 - 8 .
7. Bartel DP . MicroRNAs: genomics , biogenesis, mechanism, and function. Cell . 2004 ; 116 ( 2 ): 281 - 97 .
8. Christenson LK . MicroRNA control of ovarian function . Anim Reprod/Colegio Brasileiro de Reproducao Animal . 2010 ; 7 ( 3 ): 129 - 33 .
9. Hong X , Luense LJ , McGinnis LK , Nothnick WB , Christenson LK. Dicer1 is essential for female fertility and normal development of the female reproductive system . Endocrinology . 2008 ; 149 ( 12 ): 6207 - 12 .
10. Nagaraja AK , Andreu-Vieyra C , Franco HL , Ma L , Chen R , Han DY , et al. Deletion of Dicer in somatic cells of the female reproductive tract causes sterility . Mol Endocrinol . 2008 ; 22 ( 10 ): 2336 - 52 .
11. Otsuka M , Zheng M , Hayashi M , Lee JD , Yoshino O , Lin S , et al. Impaired microRNA processing causes corpus luteum insufficiency and infertility in mice . J Clin Invest . 2008 ; 118 ( 5 ): 1944 - 54 .
12. Lei L , Jin S , Gonzalez G , Behringer RR , Woodruff TK . The regulatory role of Dicer in folliculogenesis in mice . Mol Cell Endocrinol . 2010 ; 315 ( 1-2 ): 63 - 73 .
13. He L , Hannon GJ . MicroRNAs: small RNAs with a big role in gene regulation . Nat Rev Genet . 2004 ; 5 ( 7 ): 522 - 31 .
14. Ambros V. The functions of animal microRNAs . Nature . 2004 ; 431 ( 7006 ): 350 - 5 .
15. Bartel DP . MicroRNAs: target recognition and regulatory functions . Cell . 2009 ; 136 ( 2 ): 215 - 33 .
16. Carletti MZ , Christenson LK. MicroRNA in the ovary and female reproductive tract . J Anim Sci . 2009 ; 87 (14 Suppl): E29 - 38 .
17. Teague EM , Print CG , Hull ML . The role of microRNAs in endometriosis and associated reproductive conditions . Hum Reprod Update . 2010 ; 16 ( 2 ): 142 - 65 .
18. Papaioannou MD , Nef S. microRNAs in the testis: building up male fertility . J Androl . 2010 ; 31 ( 1 ): 26 - 33 .
19. Perheentupa A , Huhtaniemi I. Aging of the human ovary and testis . Mol Cell Endocrinol . 2009 ; 299 ( 1 ): 2 - 13 .
20. Lau NC , Lim LP , Weinstein EG , Bartel DP . An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans . Science . 2001 ; 294 ( 5543 ): 858 - 62 .
21. Lai EC . microRNAs: runts of the genome assert themselves . Curr Biol . 2003 ; 13 ( 23 ): R925 - 36 .
22. Plasterk RH . Micro RNAs in animal development . Cell . 2006 ; 124 ( 5 ): 877 - 81 .
23. Ahn HW , Morin RD , Zhao H , Harris RA , Coarfa C , Chen ZJ , et al. MicroRNA transcriptome in the newborn mouse ovaries determined by massive parallel sequencing . Mol Hum Reprod . 2010 ; 16 ( 7 ): 463 - 71 .
24. Yao G , Liang M , Liang N , Yin M , Lu M , Lian J , et al. MicroRNA-224 is involved in the regulation of mouse cumulus expansion by targeting Ptx3 . Mol Cell Endocrinol . 2013 ; 382 ( 1 ): 244 - 53 .
25. Carletti MZ , Fiedler SD , Christenson LK. MicroRNA 21 blocks apoptosis in mouse periovulatory granulosa cells . Biol Reprod . 2010 ; 83 ( 2 ): 286 - 95 .
26. Yan G , Zhang L , Fang T , Zhang Q , Wu S , Jiang Y , et al. MicroRNA-145 suppresses mouse granulosa cell proliferation by targeting activin receptor IB . FEBS Lett . 2012 ; 586 ( 19 ): 3263 - 70 .
27. Ro S , Song R , Park C , Zheng H , Sanders KM , Yan W. Cloning and expression profiling of small RNAs expressed in the mouse ovary . RNA . 2007 ; 13 ( 12 ): 2366 - 80 .
28. Mishima T , Takizawa T , Luo SS , Ishibashi O , Kawahigashi Y , Mizuguchi Y , et al. MicroRNA (miRNA) cloning analysis reveals sex differences in miRNA expression profiles between adult mouse testis and ovary . Reproduction . 2008 ; 136 ( 6 ): 811 - 22 .
29. Audic S , Claverie JM . The significance of digital gene expression profiles . Genome Res . 1997 ; 7 ( 10 ): 986 - 95 .
30. Bullard JH , Purdom E , Hansen KD , Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments . BMC Bioinformatics . 2010 ; 11 : 94 .
31. Robinson MD , McCarthy DJ , Smyth GK . edgeR: a Bioconductor package for differential expression analysis of digital gene expression data . Bioinformatics . 2010 ; 26 ( 1 ): 139 - 40 .
32. Langmead B , Hansen KD , Leek JT . Cloud-scale RNA-sequencing differential expression analysis with Myrna . Genome Biol . 2010 ; 11 ( 8 ): R83 .
33. Anders S , Huber W. Differential expression analysis for sequence count data . Genome Biol . 2010 ; 11 ( 10 ): R106 .
34. Livak KJ , Schmittgen TD . Analysis of relative gene expression data using real-time quantitative PCR and the 2(− Delta Delta C(T)) Method. Methods . 2001 ; 25 ( 4 ): 402 - 8 .
35. Friedlander MR , Chen W , Adamidi C , Maaskola J , Einspanier R , Knespel S , et al. Discovering microRNAs from deep sequencing data using miRDeep . Nat Biotechnol . 2008 ; 26 ( 4 ): 407 - 15 .
36. Dhahbi JM , Atamna H , Boffelli D , Magis W , Spindler SR , Martin DI . Deep sequencing reveals novel microRNAs and regulation of microRNA expression during cell senescence . PLoS One . 2011 ; 6 ( 5 ): e20509 .
37. Hossain MM , Ghanem N , Hoelker M , Rings F , Phatsara C , Tholen E , et al. Identification and characterization of miRNAs expressed in the bovine ovary . BMC Genomics . 2009 ; 10 : 443 .
38. Kang L , Cui X , Zhang Y , Yang C , Jiang Y. Identification of miRNAs associated with sexual maturity in chicken ovary by Illumina small RNA deep sequencing . BMC Genomics . 2013 ; 14 : 352 .
39. Betel D , Koppal A , Agius P , Sander C , Leslie C. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites . Genome Biol . 2010 ; 11 ( 8 ): R90 .
40. Betel D , Wilson M , Gabow A , Marks DS , Sander C. The microRNA . org resource: targets and expression . Nucleic Acids Res . 2008 ; 36 (Database issue): D149 - 53 .
41. John B , Enright AJ , Aravin A , Tuschl T , Sander C , Marks DS . Human MicroRNA targets . PLoS Biol . 2004 ; 2 ( 11 ): e363 .
42. Enright AJ , John B , Gaul U , Tuschl T , Sander C , Marks DS . MicroRNA targets in Drosophila . Genome Biol . 2003 ; 5 ( 1 ): R1 .
43. Dweep H , Sticht C , Pandey P , Gretz N. miRWalk-database: prediction of possible miRNA binding sites by “walking” the genes of three genomes . J Biomed Inform . 2011 ; 44 ( 5 ): 839 - 47 .
44. Li M , Liu Y , Wang T , Guan J , Luo Z , Chen H , et al. Repertoire of porcine microRNAs in adult ovary and testis by deep sequencing . Int J Biol Sci . 2011 ; 7 ( 7 ): 1045 - 55 .
45. Huang J , Ju Z , Li Q , Hou Q , Wang C , Li J , et al. Solexa sequencing of novel and differentially expressed microRNAs in testicular and ovarian tissues in Holstein cattle . Int J Biol Sci . 2011 ; 7 ( 7 ): 1016 - 26 .
46. Tesfaye D , Worku D , Rings F , Phatsara C , Tholen E , Schellander K , et al. Identification and expression profiling of microRNAs during bovine oocyte maturation using heterologous approach . Mol Reprod Dev . 2009 ; 76 ( 7 ): 665 - 77 .
47. Reid JG , Nagaraja AK , Lynn FC , Drabek RB , Muzny DM , Shaw CA , et al. Mouse let-7 miRNA populations exhibit RNA editing that is constrained in the 5'-seed/ cleavage/anchor regions and stabilize predicted mmu-let7a:mRNA duplexes . Genome Res . 2008 ; 18 ( 10 ): 1571 - 81 .
48. Pasquinelli AE , Reinhart BJ , Slack F , Martindale MQ , Kuroda MI , Maller B , et al. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA . Nature . 2000 ; 408 ( 6808 ): 86 - 9 .
49. Landgraf P , Rusu M , Sheridan R , Sewer A , Iovino N , Aravin A , et al. A mammalian microRNA expression atlas based on small RNA library sequencing . Cell . 2007 ; 129 ( 7 ): 1401 - 14 .
50. Fiedler SD , Carletti MZ , Hong X , Christenson LK . Hormonal regulation of MicroRNA expression in periovulatory mouse mural granulosa cells . Biol Reprod . 2008 ; 79 ( 6 ): 1030 - 7 .
51. Kim YJ , Ku SY , Kim YY , Liu HC , Chi SW , Kim SH , et al. MicroRNAs transfected into granulosa cells may regulate oocyte meiotic competence during in vitro maturation of mouse follicles . Hum Reprod . 2013 ; 28 ( 11 ): 3050 - 61 .
52. Eramaa M , Hilden K , Tuuri T , Ritvos O. Regulation of inhibin/activin subunit messenger ribonucleic acids (mRNAs) by activin A and expression of activin receptor mRNAs in cultured human granulosa-luteal cells . Endocrinology . 1995 ; 136 ( 10 ): 4382 - 9 .
53. Izadyar F , Dijkstra G , Van Tol HT , Van den Eijnden-van Raaij AJ , Van den Hurk R , Colenbrander B , et al. Immunohistochemical localization and mRNA expression of activin, inhibin, follistatin, and activin receptor in bovine cumulus-oocyte complexes during in vitro maturation . Mol Reprod Dev . 1998 ; 49 ( 2 ): 186 - 95 .
54. Cutting AD , Bannister SC , Doran TJ , Sinclair AH , Tizard MV , Smith CA . The potential role of microRNAs in regulating gonadal sex differentiation in the chicken embryo . Chromosome Res . 2012 ; 20 ( 1 ): 201 - 13 .
55. Knight PG , Glister C. TGF-beta superfamily members and ovarian follicle development . Reproduction . 2006 ; 132 ( 2 ): 191 - 206 .
56. Real FM , Sekido R , Lupianez DG , Lovell-Badge R , Jimenez R , Burgos M. A microRNA (mmu-miR-124) prevents Sox9 expression in developing mouse ovarian cells . Biol Reprod . 2013 ; 89 ( 4 ): 78 .
57. Alexiou P , Maragkakis M , Papadopoulos GL , Reczko M , Hatzigeorgiou AG . Lost in translation: an assessment and perspective for computational microRNA target identification . Bioinformatics . 2009 ; 25 ( 23 ): 3049 - 55 .
58. Reinhart BJ , Slack FJ , Basson M , Pasquinelli AE , Bettinger JC , Rougvie AE , et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans . Nature . 2000 ; 403 ( 6772 ): 901 - 6 .
59. Liang N , Xu Y , Yin Y , Yao G , Tian H , Wang G , et al. Steroidogenic factor-1 is required for TGF-beta3-mediated 17beta-estradiol synthesis in mouse ovarian granulosa cells . Endocrinology . 2011 ; 152 ( 8 ): 3213 - 25 .
60. Findlay JK , Drummond AE , Dyson M , Baillie AJ , Robertson DM , Ethier JF . Production and actions of inhibin and activin during folliculogenesis in the rat . Mol Cell Endocrinol . 2001 ; 180 ( 1-2 ): 139 - 44 .
61. Souza CJ , Campbell BK , McNeilly AS , Baird DT . Effect of bone morphogenetic protein 2 (BMP2) on oestradiol and inhibin A production by sheep granulosa cells, and localization of BMP receptors in the ovary by immunohistochemistry . Reproduction . 2002 ; 123 ( 3 ): 363 - 9 .
62. Juanchich A , Le Cam A , Montfort J , Guiguen Y , Bobe J. Identification of differentially expressed miRNAs and their potential targets during fish ovarian development . Biol Reprod . 2013 ; 88 ( 5 ): 128 .