Physiol Genomics 46: 735–745, 2014.
First published August 5, 2014; doi:10.1152/physiolgenomics.00036.2014.
Expression of microRNAs and their target genes and pathways associated
with ovarian follicle development in cattle
A. E. Zielak-Steciwko,1 J. A. Browne,2 P. A. McGettigan,2 M. Gajewska,3 M. Dzie˛cioł,4 T. Szulc,1
and A. C. O. Evans2
1
Institute of Animal Breeding, Wrocław University of Environmental and Life Sciences, Wrocław, Poland; 2School of
Agriculture and Food Science, University College Dublin, Belfield, Dublin, Ireland; 3Department of Physiological Sciences,
Warsaw University of Life Sciences, Warsaw, Poland; and 4Department of Reproduction and Clinic of Farm Animals,
Wrocław University of Environmental and Life Sciences, Wrocław, Poland
Submitted 20 March 2014; accepted in final form 4 August 2014
microRNA; targets; signaling; ovarian follicles
IN CATTLE, FOLLICLE DEVELOPMENT beyond the early antral stage
is characterized by two or three successive waves of follicular
growth in each estrous cycle. During follicular wave, a single
follicle is selected to continue growing and becomes dominant,
while other subordinate follicles undergo atresia (18). It is well
established that the fate of the growing follicles (ovulation or
atresia) is regulated by the interaction of endocrine signals
(e.g., gonadotropins, their receptors and steroids) and intraovarian molecules [e.g., IGF-I, transforming growth factor
(TGF)-␤] produced in granulosa and theca cells (31, 34, 22).
These factors are coordinated by expression of numerous
genes. Any alteration in the activity of these genes might be
critical in determining the survival of dominant follicles or may
play a role in the demise of subordinate follicles (6, 13, 15, 30).
Recent studies have shown that microRNAs (miRNAs) have
a significant impact on gene expression in a variety of tissues
and biological processes, both in humans (12) and animals (14,
40). These small noncoding RNA molecules function via
partial base pairing of their seed region (2- to 7-nucleotide-long
region from the 5=-end of the miRNA) with complementary
sequences to the 3=-untranslated region (UTR) of their target
genes (1, 5). This interaction usually results in gene silencing
through translation repression or direct mRNA degradation (3).
Recent findings have proven that miRNAs are important regulators of development, cell proliferation, and apoptosis and
are involved in many physiological processes, including reproduction (2). It is anticipated that these molecules are an
important element in the mechanism of the regulation of
ovarian follicle development in mammals.
Several studies have reported that a number of miRNAs are
involved in murine granulosa cell proliferation (43, 44) and
estradiol production (45). Similar observations were made in
porcine granulosa cells where miRNAs regulate estradiol production and apoptosis (26, 42). Furthermore, miRNAs have also been
detected in ovine granulosa and theca cells at different stages of
follicle development (28) and suggested to play a role in the
regulation of cell survival, steroidogenesis, and follicle differentiation in equine ovaries (36). To date, there is little known about
the role of miRNAs in bovine follicle development and in particular in dominant follicle selection. In an earlier study, miRNAs
were identified in whole bovine ovaries without distinguishing
specific tissue compartment and follicle classes (19). Recently two
reports have indicated expression of miRNAs in bovine granulosa
cells from various sized follicles (32) and in cultured bovine theca
and granulosa cells (28).
Considering the limited knowledge on the role of miRNAs
in bovine ovarian follicle development and in dominant follicle
selection the aim of the present work was: 1) to determine if
miRNAs are expressed locally within bovine antral follicles
and to localize expression within the theca and/or granulosa
layer, 2) to determine if the pattern of miRNA expression is
different between the dominant and the largest subordinate
follicle from the first follicle wave of the cycle, and 3) to use
a bioinformatic approach to identify the potential pathways and
putative targets for the selected miRNAs involved in bovine
follicle development.
MATERIAL AND METHODS
Address for reprint requests and other correspondence: A. Zielak-Steciwko,
Inst. of Animal Breeding, Wrocław Univ. of Environmental and Life Sciences,
ul. Chełmońskiego 38c, 50-375 Wrocław, Poland (e-mail: anna.zielak
@up.wroc.pl).
Animal study and tissue collection. To obtain ovarian follicles from
the first wave, six cross-breed beef heifers were subjected to an estrus
synchronization program combined with daily ultrasonography of
1094-8341/14 Copyright © 2014 the American Physiological Society
735
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
Zielak-Steciwko AE, Browne JA, McGettigan PA, Gajewska
M, Dzie˛cioł M, Szulc T, Evans ACO. Expression of microRNAs and
their target genes and pathways associated with ovarian follicle development in cattle. Physiol Genomics 46: 735–745, 2014. First published
August 5, 2014; doi:10.1152/physiolgenomics.00036.2014.—Development of ovarian follicles is controlled at the molecular level by several
gene products whose precise expression leads to regression or ovulation of follicles. MicroRNAs (miRNAs) are small noncoding RNAs
that regulate gene expression through sequence-specific base pairing
with target messenger RNAs (mRNAs) causing translation repression
or mRNA degradation. The aim of this study was to identify miRNAs
expressed in theca and/or granulosa layers and their putative target
genes/pathways that are involved in bovine ovarian follicle development. By using miRCURY microarray (Exiqon) we identified 14 and
49 differentially expressed miRNAs (P ⬍ 0.01) between dominant
and subordinate follicles in theca and granulosa cells, respectively.
The expression levels of four selected miRNAs were confirmed by
qRT-PCR. To identify target prediction and pathways of differentially
expressed miRNAs we used Union of Genes option in DIANA
miRPath v.2.0 software. The predicted targets for these miRNAs were
enriched for pathways involving oocyte meiosis, Wnt, TGF-beta,
ErbB, insulin, P13K-Akt, and MAPK signaling pathways. This study
identified differentially expressed miRNAs in the theca and granulosa
cells of dominant and subordinate follicles and implicates them in
having important roles in regulating known molecular pathways that
determine the fate of ovarian follicle development.
736
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
Green Master Mix (Applied Biosystems, Warrington, UK). Primers
were designed for each gene of interest (Primer Express Software
v2.0, Applied Biosystems): CYP19A1 (forward: 5=-TGG TGA CCA
TCT GTG CTG AT-3= and reverse: 5=-GTC AAC ACG TCC ACA
TAG CC-3=) and CYP17A1 (forward: 5=-CTG GAG GTT CTG AGC
AAG GA-3= and reverse: 5=-TGG CTT TGC TGG GGA AAA
TC-3=). The specificity of all primers was confirmed both by meltcurve analysis and by agarose gel electrophoresis of the amplified
PCR fragments. Primer efficiency was determined using a serial
dilution of Bos taurus-derived cDNA (1:4 dilution series over 7
points) and shown to lie between 90 and 110%. The cDNA prepared
for the miRNA study (of each sample) was also used to perform the
mRNA analysis. The optimal number of reference targets for this
sample set were identified using the geNorm application within the
qbasePLUS software package (Biogazelle, Zwijnaarde, Belgium). The
normalization factor was calculated as the geometric mean of reference targets YWHAZ (forward: 5=-TGA AGC CAT TGC TGA ACT
TG-3= and reverse: 5=-TCT CCT TGG GTA TCC GAT GT-3=) and
PPIA (forward: 5=-CAT ACA GGT CCT GGC ATC TTG TCC-3=
and reverse: 5=-CAC GTG CTT GCC ATC CAA CC-3=). Calibrated
normalized relative quantities of gene expression for each analyzed
sample were generated by the qbasePLUS package and analyzed
within the package using an unpaired t-test.
MiRNA expression profiling was performed by Exiqon (Exiqon,
Vedbaek, Denmark). The samples from each tissue were analyzed
separately. Two tissue pools comprising RNA from all granulosa
samples and all theca samples respectively were generated and used as
the control sample on each slide. There were six samples for each
follicle type (dominant and subordinate) within each tissue (theca and
granulosa) for a total of 24 slides. In brief, the samples were labeled
using the miRCURY LNA microRNA Hi-Power Labeling Kit, Hy3/
Hy5, and hybridized on the miRCURY LNA microRNA Array 6th
Gen (Exiqon). Slides were scanned using the Agilent G2565BA
Microarray Scanner System (Agilent Technologies), and the image
analysis was conducted in ImaGene 9 (miRCURY LNA microRNA
Array Analysis Software, Exiqon). Raw file data were analyzed using
the limma R-package (37). After background correction (method ⫽
“subtract”) and normalization of samples (normalizeWithinArrays no offset), probes with no name, control probes, and spike in probes
were eliminated from further analysis. One sample (granulosa subordinate follicle) exhibited an expression profile markedly different
from all the other samples and was excluded from the granulosa
analysis. Analysis of differential expression was performed by lmFit
Table 1. MiRNAs in theca cells that were differentially expressed in dominant compared with the largest subordinate
follicles
Microarray
MiRNA Name
Accession Number
Sequence
Ratio
FDR P Value
hsa-miR-301b/bta-miR-301b
hsa-miR-190b/bta-miR-190b
hsa-miR-1301
hsa-miRPlus-I181b-2*
hsa-miR-1255b-5p
hsa-miR-1184
hsa-miR-let-7i-3p
hsa-miR-129-2-3p/bta-miR-129-3p
hsa-miR-548aa/hsa-miR-548t-3
hsa-miR-3684
hsa-miR-29b-1-5p
hsa-miR-302e
hsa-miR-196a-3p
has-miR-1284
MIMAT0004958
MIMAT0004929
MIMAT0005797
MIMAT0017084
MIMAT0005945
MIMAT0005829
MIMAT0004585
MIMAT0004605
MIMAT0018447
MIMAT0018112
MIMAT0004514
MIMAT0005931
MIMAT0004562
MIMAT0005941
cagugcaaugauauugucaaagc
ugauauguuugauauuggguu
uugcagcugccugggagugacuuc
cucacugaucaaugaaugcaaa
cggaugagcaaagaaagugguu
ccugcagcgacuugauggcuucc
cugcgcaagcuacugccuugcu
aagcccuuaccccaaaaagcau
aaaaaccacaauuacuuuugcacca
uuagaccuaguacacguccuu
gcugguuucauauggugguuuaga
uaagugcuuccaugcuu
cggcaacaagaaacugccugag
ucuauacagacccuggcuuuuc
18.44
12.84
11.59
5.11
3.98
3.74
1.24
⫺8.68
⫺5.88
⫺4.65
⫺3.97
⫺3.09
⫺3.04
⫺1.31
⬍0.001
0.008
⬍0.001
0.002
0.002
0.008
0.010
0.010
0.002
0.002
0.004
0.007
0.006
0.009
Data from heifers (n ⫽ 6) collected at dominance stage of the 1st follicular wave determined by DNA microarrays. Positive ratio values indicate greater
expression in the dominant compared with the subordinate follicle and negative values vice versa. MiRNA, microRNA; FDR, false discovery rate. In Tables 1
and 2, miRNAs in boldface were rejected from the target prediction and pathway analysis as they were not available in the database of DIANA miRPath software.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
ovaries. Animals were kept and fed under the same conditions.
Heifers were synchronized with an 8-day progesterone-releasing intravaginal device (CIDR, Pfizer Animal Health) and a single intramuscular injection (25 mg) of prostaglandin F2 alpha (Dinolityc,
Pfizer) on the seventh day. Estrus was observed every 6 h between 24
and 48 h after CIDR withdrawal. Each animal was subjected to daily,
individual ovarian ultrasound scans to monitor ovarian follicle development by real-time B mode scanner (HS-1500 Vet, Honda Electronics, Japan) with 5 MHz transrectal linear probe. Animals were slaughtered between day 2.5 and 3.5 of the synchronized estrus cycle (day 0 ⫽
onset of estrus). The two largest follicles were collected from the
ovaries of each animal, and individual follicle diameter was measured.
Follicular fluid was aspirated from the follicles, snap-frozen in liquid
nitrogen, and stored at ⫺80°C for hormone analyses. Theca and
granulosa cells were separated as previously described (13), snapfrozen, and stored at ⫺80°C prior to RNA extractions.
All experimental procedures were licensed by the 2nd Local Ethics
Committee at Wrocław University of Environmental and Life Sciences, Poland.
Estradiol and progesterone concentrations were measured using
commercially available kits Coat-A-Count Estradiol RIA and CoatA-Count Progesterone RIA kit, respectively (Siemens Healthcare
Diagnostics, Tarrytown, NY). Based on the intrafollicular ratio of estradiol to progesterone, follicles were hormonally classified as estrogen
active follicles (dominant) - ratios of ⬎ 1 or atretic follicles (subordinate)
- ratios of ⬍ 1 (20). Follicle diameter and follicular fluid hormone
concentrations between the two follicles from each individual were
analyzed by ANOVA using the SAS 9.2 software. Results are shown as
means ⫾ SE. Statistical significance was accepted when P ⬍ 0.05.
RNA isolation, miRNA microarray, and data analysis. Total RNA,
including small RNA was isolated from theca and granulosa samples
using miRNeasy Mini Kit (Qiagen, Hilden, Germany) according to
the manufacturer’s instructions followed by DNase treatment step
using RNase-Free DNase set (Qiagen). Total RNA quantity was
determined by NanoDrop ND-1000 spectrophotometer (NanoDrop
Technologies, Wilmington, DE) and quality was assessed using the
Agilent Bioanalyzer 2100 with RNA 6000 Nano Chip kit (Agilent
Technologies, Santa Clara, CA). All RNA samples were shown to
have RNA integrity number values ⬎ 7.5.
To exclude cross-contamination between theca and granulosa cells,
we verified the mRNA expression profiles of CYP19A1 and
CYP17A1 genes by quantitative real-time PCR (qRT-PCR) using the
ABI Prism 7500 FAST sequence detection system and Fast SYBR
737
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
and empirical Bayes functions in Limma package of Bioconductor
(38). The Benjamini-Hochberg correction was used to control for false
discovery rate (FDR). The FDR was set at 1% (4). The raw data from
the experiment were deposited in the Gene Expression Omnibus
repository under accession number GSE55890.
miRNA target gene prediction and pathway analysis. Differentially
expressed miRNAs (P ⬍ 0.01), between dominant and subordinate
follicles in theca and granulosa cells, recognized by microarray, were
further analyzed to identify their target prediction and pathway analysis. For this purpose, the Union of Genes option, in the second
version of DIANA miRPath (41) software, was used. This computational tool performs an analysis between the miRNAs and targeted
genes compiled in UNION_SET. Enrichment investigation identi-
fies pathways significantly enriched with genes belonging to the
UNION_SET (a priori method) in Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathways (21a). Prediction was performed by
using DIANA-microT-CDS with MicroT threshold set to 0.8 score.
Benjamini and Hochberg’s FDR was applied with significant threshold set at P ⬍ 0.01 (except for hsa-miR-18a-5p/bta-miR-18a for
which significant threshold was set at P ⬍ 0.05, because no predicted
pathways were found at P ⬍ 0.01).
qRT-PCR analysis of miRNAs. Bovine miRNA sequences are not
integrated with the current version of DIANA web server. Therefore,
four potentially differentially expressed miRNAs (hsa-miR-301b/btamiR-301b, hsa-miR-129-2-3p/bta-miR-129-3p, hsa-miR-18a-5p/btamiR-18a, and hsa-miR-582-5p/bta-miR-582), which were human and
Microarray
MiRNA Name
Accession Number
hsa-miR-548b-3p
hsa-miR-212-5p/bta-miR-212
hsa-miR-3689b-3p/hsa-miR-3689c
hsa-miR-548h-5p
hsa-miR-33a-3p
hsa-miR-564
hsa-miR-671-5p/bta-miR-671
hsa-miR-3153
hsa-miRPlus-G1140-3p
hsa-miR-582-5p/bta-miR-582
hsa-miR-1226-3p
hsa-miR-597
hsa-miR-190b/bta-miR-190b
hsa-miR-3175
hsa-miR-4292
hsa-miR-125a-3p
hsa-miR-18a-5p/bta-miR-18a
hsa-miR-569
hsa-miR-3190-3p
hsa-miR-3605-3p
hsa-miR-93-3p
hsa-miR-548t-5p
hsa-miR-3663-3p
hsa-miR-216a/bta-miR-216a
hsa-miR-524-5p
hsa-miR-3146
hsa-miR-30d-3p
hsa-miR-148b-5p
hsa-miR-555
hsa-miR-887
hsa-miRPlus-C1087
hsa-miR-4254
hsa-miR-219-5p/bta-miR-219-5p
hsa-miR-582-5p/bta-miR-582
hsa-miR-583
hsa-miR-520f
hsa-miR-615-5p/bta-miR-615
hsa-miR-548h-3p/hsa-miR-548z
hsa-miR-4314
hsa-miR-185-3p
hsa-miR-26a-1-3p
bta-miR-362-5p
hsa-miR-3178
hsa-miR-1469
hsa-miR-4285
hsa-miR-659-3p
hsa-miR-4288
hsa-miR-625-3p
hsa-miR-3195
MIMAT0003254
MIMAT0022695
MIMAT0018181
MIMAT0005928
MIMAT0004506
MIMAT0003228
MIMAT0003880
MIMAT0015026
N/A
MIMAT0003247
MIMAT0005577
MIMAT0003265
MIMAT0004929
MIMAT0015052
MIMAT0016919
MIMAT0004602
MIMAT0000072
MIMAT0003234
MIMAT0022839
MIMAT0017982
MIMAT0004509
MIMAT0015009
MIMAT0018085
MIMAT0000273
MIMAT0002849
MIMAT0015018
MIMAT0004551
MIMAT0004699
MIMAT0003219
MIMAT0004951
N/A
MIMAT0016884
MIMAT0000276
MIMAT0003247
MIMAT0003248
MIMAT0026609
MIMAT0004804
MIMAT0022723
MIMAT0016868
MIMAT0004611
MIMAT0004499
MIMAT0009298
MIMAT0015055
MIMAT0007347
MIMAT0016913
MIMAT0003337
MIMAT0016918
MIMAT0004808
MIMAT0015079
Sequence
caagaaccucaguugcuuuugu
accuuggcucuagacugcuuacu
cugggaggugugauauuguggu
aaaaguaaucgcgguuuuuguc
caauguuuccacagugcaucac
aggcacggugucagcaggc
aggaagcccuggaggggcuggag
ggggaaagcgaguagggacauuu
N/A
uuacaguuguucaaccaguuacu
ucaccagcccuguguucccuag
ugugucacucgaugaccacugu
ugauauguuugauauuggguu
cggggagagaacgcagugacgu
ccccugggccggccuugg
acaggugagguucuugggagcc
uaaggugcaucuagugcagauag
aguuaaugaauccuggaaagu
uguggaagguagacggccagaga
ccuccguguuaccuguccucuag
acugcugagcuagcacuucccg
caaaagugaucgugguuuuug
ugagcaccacacaggccgggcgc
uaaucucagcuggcaacuguga
cuacaaagggaagcacuuucuc
caugcuaggauagaaagaaugg
cuuucagucagauguuugcugc
aaguucuguuauacacucaggc
aggguaagcugaaccucugau
gugaacgggcgccaucccgagg
N/A
gccuggagcuacuccaccaucuc
ugauuguccaaacgcaauucu
uuacaguuguucaaccaguuacu
caaagaggaaggucccauuac
ccucuaaagggaagcgcuuucu
ggggguccccggugcucggauc
caaaaaccgcaauuacuuuugca
cucugggaaaugggacag
aggggcuggcuuuccucugguc
ccuauucuugguuacuugcacg
aauccuuggaaccuaggugugagu
ggggcgcggccggaucg
cucggcgcggggcgcgggcucc
gcggcgaguccgacucau
cuugguucagggagggucccca
uugucugcugaguuucc
gacuauagaacuuucccccuca
cgcgccgggcccggguu
Ratio
18.08
15.98
14.13
12.33
11.15
10.28
9.77
6.85
4.82
3.56
1.88
1.55
1.41
1.39
1.36
1.35
1.32
⫺13.03
⫺11.14
⫺10.96
⫺10.34
⫺10.13
⫺9.72
⫺9.00
⫺8.59
⫺8.25
⫺7.11
⫺6.71
⫺5.60
⫺4.84
⫺4.66
⫺4.53
⫺4.50
⫺4.50
⫺3.60
⫺3.53
⫺3.51
⫺3.28
⫺2.60
⫺2.11
⫺1.04
⫺1.01
⫺1.84
⫺1.53
⫺1.46
⫺1.43
⫺1.41
⫺1.40
⫺1.31
FDR P Value
⬍0.001
0.005
0.006
0.007
0.004
0.001
⬍0.001
⬍0.001
⬍0.001
0.002
0.006
0.009
0.009
0.002
0.007
0.009
0.009
0.007
0.001
⬍0.001
0.009
0.009
0.009
0.010
⬍0.001
0.001
0.001
0.005
⬍0.001
0.001
⬍0.001
⬍0.001
0.004
0.007
0.007
⬍0.001
0.002
0.005
0.001
0.009
0.006
0.002
⬍0.001
0.007
0.005
⬍0.001
0.008
0.001
0.008
Data from heifers (n ⫽ 6) collected at dominance stage of the first follicular wave determined using DNA microarrays. Positive ratio values indicate greater
expression in the dominant compared with the subordinate follicle and negative values vice versa. N/A, not available.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
Table 2. MiRNAs in granulosa cells that were differentially expressed in dominant compared with the largest subordinate
follicles
738
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
Table 3. Selected miRNAs differentially expressed between dominant and the largest subordinate follicles in theca and
granulosa cells from heifers (n ⫽ 6) collected at dominance stage of the first follicular wave determined by microarrays and
qRT-PCR
Microarray
qRT-PCR
MiRNA name
Accession Number
Tissue Type
Ratio
P Value
Ratio
P Value
hsa-miR-301b/bta-miR-301b
hsa-miR-129-2-3p/bta-miR-129-3p
hsa-miR-18a-5p/bta-miR-18a
hsa-miR-582-5p/bta-miR-582
MIMAT0004958
MIMAT0004605
MIMAT0000072
MIMAT0003247
TC
TC
GC
GC
18.44
⫺8.68
1.32
⫺4.49
⬍0.001
0.010
0.008
0.007
2.82 ⫾ 0.36
⫺2.06 ⫾ 0.55
1.90 ⫾ 0.11
⫺1.35 ⫾ 0.45
0.003
0.047
0.047
0.049
TC, theca cell; GC, granulosa cell.
RESULTS
miRNA expression profiling in bovine ovarian follicles. To
establish miRNA expression patterns, we collected ovarian
Table 4. Significantly enriched signaling pathways (P ⬍
0.01) associated with differentially expressed miRNAs in
theca cells between dominant and subordinate follicles
Pathway Name
Dominant Follicles
Oocyte meiosis
Wnt signaling pathway
TGF-beta signaling pathway
Protein processing in endoplasmic reticulum
RNA degradation
Axon guidance
Pathways in cancer
Subordinate Follicles
Neurotropin signaling pathway
ErbB signaling pathway
Pathways in cancer
Insulin signaling pathway
Endocytosis
Chemokine signaling pathway
Regulation of actin cytoskeleton
HTLV-I infection
Epstein-Barr virus infection
Pathway ID
FDR*
hsa04114
hsa04310
hsa04350
hsa04141
hsa03018
hsa04360
hsa05200
0.0004
0.0016
0.0016
0.0038
0.0043
0.0050
0.0084
hsa04722
hsa04012
hsa05200
hsa04910
hsa04144
hsa04062
hsa04810
hsa05166
hsa05169
5.32E-10
5.93E-09
1.02E-07
1.44E-07
6.20E-06
6.92E-06
2.57E-05
0.0044
0.0066
Dominant follicles: hsa-miR-301b/bta-miR-301b, hsa-miR-190b, hsa-miR1301, hsa-miR-1255b-5p, hsa-miR-1184. Subordinate follicles: hsa-miR-1292-3p, hsa-miR-548aa, hsa-miR-3684, hsa-miR-29b-1-5p, hsa-miR-302e, hsamiR-196a-3p. *FDR correction was calculated with Benjamini-Hochberg with
a threshold of 0.01 or 0.05 as indicated.
follicles at dominance stage of the first follicular wave. Mean
follicle diameter was greater in the dominant follicle (10.7 ⫾
0.3 mm, n ⫽ 6) compared with the subordinate follicles (8.7 ⫾
0.2 mm, P ⬍ 0.01, n ⫽ 6). Follicular fluid estradiol concentrations also were higher (P ⬍ 0.01) in the dominant (177.4 ⫾
26.8 ng/ml, n ⫽ 6) compared with the largest subordinate
follicles (17.8 ⫾ 5.6 ng/ml, n ⫽ 6). In contrast progesterone
concentrations were higher (P ⬍ 0.02) in the largest subordinate (100.6 ⫾ 13.8 ng/ml, n ⫽ 6) compared with the dominant
follicles (63.4 ⫾ 5.1 ng/ml, n ⫽ 6).
Table 5. Significantly enriched signaling pathways (P ⬍
0.01) associated with the differentially expressed miRNAs in
granulosa cells between dominant and subordinate follicles
Pathway Name
Pathway ID
Dominant Follicles
Neurotropin signaling pathway
hsa04722
Gap junction
hsa04540
Endocytosis
hsa04144
PI3K-Akt signaling pathway
hsa04151
MAPK signaling pathway
hsa04010
Focal adhesion
hsa04510
Transcriptional misregulation in cancer
hsa05202
Pathways in cancer
hsa05200
Vascular smooth muscle contraction
hsa04270
Regulation of actin cytoskeleton
hsa04810
Chemokine signaling pathways
hsa04062
Subordinate Follicles
Wnt signaling pathway
hsa04310
MAPK signaling pathway
hsa04010
Ubiquitin mediated proteolisis
hsa04120
Dopaminergic synapse
hsa04728
Neurotropin signaling pathway
hsa04722
Axon guidance
hsa04360
mRNA surveillance pathway
hsa03015
Pathways in cancer
hsa05200
PI3K-Akt signaling pathway
hsa04151
Osteoclast differentiation
hsa04380
HTLV-I infection
hsa05166
Endocytosis
hsa04144
RNA transport
hsa03013
Transcriptional misregulation in cancer
hsa05202
Cell cycle pathways
hsa04110
FDR*
1.15E-12
3.62E-11
3.20E-08
3.63E-08
1.22E-07
1.22E-07
2.41E-07
2.92E-07
2.16E-06
0.0018
0.0073
6.02E-38
4.25E-34
3.63E-30
7.13E-29
9.79E-27
1.44E-25
1.44E-21
2.37E-21
6.79E-19
2.92E-17
5.65E-14
4.73E-11
2.80E-10
5.29E-06
0.0023
Dominant follicles: hsa-miR-548b-3p, hsa-miR-212-5p, hsa-miR-3689b-3p,
hsa-miR-548h-5p, hsa-miR-33a-3p, hsa-miR-564, hsa-miR-671-5p, hsa-miR3153, hsa-miR-582-5p, hsa-miR-1226-3p, hsa-miR-597, hsa-miR-3175, hsamiR-4292, hsa-miR-125a-3p, hsa-miR-18a-5p, hsa-miR-190b. Subordinate
follicles: hsa-miR-4288, hsa-miR-659-3p, hsa-miR-185-3p, hsa-miR-4314,
hsa-miR-548h-3p, hsa-miR-520f, hsa-miR-583, hsa-miR-582-5p, hsa-miR219-5p, hsa-miR-555, hsa-miR-148b-5p, hsa-miR-30d-3p, hsa-miR-3146, hsamiR-524-5p, hsa-miR-3663-3p, hsa-miR-548t-5p, hsa-miR-93-3p, hsa-miR3190-3p, hsa-miR-569.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
bovine orthologs, were validated by qRT-PCR. MiRNA expression
profiling was performed using the miScript system (Qiagen, Hilden,
Germany) as per manufacturer’s instructions. RNU6 was used as
internal control. In brief, 1,000 ng of total RNA was reverse transcribed using the miScript II Reverse Transcriptase in a 20 ␮l
reaction; this was subsequently diluted 1:40 using RNase/DNase-free
water. qRT-PCR was conducted using a 7500 Fast Real-Time PCR
machine (Applied Biosystem, Foster City, CA) in 96-well plates using
miScript SYBER Green PCR Kit (Qiagen); each reaction (25 ␮l)
contained 12.5 ␮l of 2⫻ QuantiTec SYBER Green PCR Master Mix,
2.5 ␮l of 10⫻ miScript Universal Primer, 2.5 ␮l of 10⫻ miScript
Primer Assay, and 5 ␮l of the diluted template cDNA. Dissociation
curves of PCR reactions were monitored to ensure a single specific
PCR product, and the appropriate negative and positive controls were
included. Changes in relative concentration were calculated with the
qBasePLUS Software (Biogazelle), and relative expression levels
were compared by ANOVA using the SAS 9.2 software. Results are
expressed as means ⫾ SE. Statistical significance was accepted when
P ⬍ 0.05.
739
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
Cross-contamination analysis between theca and granulosa
cells revealed CYP19A1 amplicons in granulosa cells but not
in theca cells. While amplicons of CYP17A1 were only detected in theca cell (data not shown).
The microarray contained a total of 1,488 bovine and human
miRNAs, from miRBase version 18 (24). Comparing the dominant with the subordinate follicle our initial analysis using a
threshold of P ⬍ 0.05 identified a total of 87 differentially
Table 6. Enrichment analysis of miR-301b putative gene targets in KEGG pathways, performed by using DIANA-microT
with significant threshold set at P ⬍ 0.01
KEGG Pathway
Prion diseases
TGF-beta signaling pathway
Phosphatidylinositol signaling system
mTOR signaling pathway
Endocytosis
Gap junction
p53 signaling pathway
Circadian rhythm
Axon guidance
Inositol phosphate metabolism
Insulin signaling pathway
Melanogenesis
Prostate cancer
Glioma
RNA degradation
Adipocytokine signaling pathway
Gene Name
FDR*
KEGG Pathway ID
PRNP
ROCK1, INHBB, SMURF2, INHBA, ACVR1, SKP1, ZFYVE9,
SMAD5, TGFB2, TGFBR2, BMPR2
DGKE, CALM2, PLCB1, PIKFYVE, PIK3C2A, PTEN, ITPK1, PLCB4
TSC1, RRAGD, PRKAA2, PDK1, PRKAA1, EIF4E2, PTEN, ULK2
RNF41, ARAP2, SMURF2, PSD, CLTC, CHMP4B, ASAP1, RAB5A,
CBLB, EPS15, ZFYVE9, TGFB2, LDLR, TGFBR2
ADCY1, SOS2, PLCB1, GJA1, ADCY4, PLCB4
ZMAT3, CDS1, GADD45A, MDM4, CDKN1A, SESN3, PTEN
PRKAA2, BHLHE41, SKP1, PRKAA1, CLOCK
EFNB2, ROCK1, ARHGEF12, ROBO2, EPHB4, DPYSL2, CFL2,
NRP1, PAK6, ROBO1
PLCB1, PIKFYVE, PIK3C2A, PTEN, ITPK1, PLCB4
TSC1, SOS2, PRKAA2, CALM2, CBLB, PDK1, PRKAA1, EIF4E2,
PPARGC1A, PPP1CB
ADCY1, TCF4, WNT2B, CALM2, PLCB1, EDN1, ADCY4, PLCB4
SOS2, E2F2, TCF4, TGFA, PDK1, CDKN1A, PTEN
SOS2, E2F2, TGFA, CALM2, CDKN1A, PTEN
CNOT6, CNOT4, DDX6, DCP2, PAN3, BTG1
ACSL4, PRKAA2, TNFRSF1B, PRKAA1, PPARGC1A
1.078737e-27
6.359019e-07
hsa05020
hsa04350
2.181797e-05
2.181797e-05
2.282662e-05
hsa04070
hsa04150
hsa04144
8.052829e-05
0.00026
0.00049
0.00094
hsa04540
hsa04115
hsa04710
hsa04360
0.0011
0.0011
hsa00562
hsa04910
0.0057
0.0068
0.0086
0.0098
0.0098
hsa04916
hsa05215
hsa05214
hsa03018
hsa04920
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
Fig. 1. KEGG map visualization of hsa-miR-301b/bta-miR-301b involvement in prion diseases signaling pathway. Green, genes not targeted by this microRNA
(miRNA); yellow, putative targets for this miRNA; red, gene especially important in ovarian follicle physiology.
740
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
Table 7. Enrichment analysis of miR-129-2-3p putative gene targets in KEGG pathways, performed by using DIANA-microT
with significant threshold set at P ⬍ 0.01
KEGG Pathway
Gene Name
FDR*
KEGG Pathway ID
Valine, leucine, and isoleucine degradation
Ubiquitin-mediated proteolysis
SNARE interactions in vesicular transport
Fatty acid metabolism
GnRH signaling pathway
Fc epsilon RI signaling pathway
Nicotinate and nicotinamide metabolism
Ubiquinone and other terpenoid-quinone biosynthesis
HIBADH, HMGCS1, ACADM
WWP1, RHOBTB1, MAP3K1, KLHL13, RCHY1
VTI1A, STX6
ACADM
MAP3K1, MAP2K6, MAP2K4
PRKC, MAP2K6, MAP2K4
NMNAT2
COQ3
0.00025
0.00036
0.00090
0.0019
0.0019
0.0030
0.0047
0.0056
hsa00280
hsa04120
hsa04130
hsa00071
hsa04912
hsa04664
hsa00760
hsa00130
1
The online version of this article contains supplemental material.
3p) were greater (P ⬍ 0.05) in the subordinate compared with
dominant follicles. Additionally, in the granulosa cells, hsamiR-18a-5p/bta-miR-18a (miR-18a-5p) expression was greater
(P ⬍ 0.05) in the dominant compared with the largest subordinate follicle, whereas hsa-miR-582-5p/bta-miR-582 (miR582-5p) expression was greater (P ⬍ 0.05) in the subordinate
compared with dominant follicles. Furthermore, to determine
whether validation by RT-PCR correlated with miRNA microarray results, we performed a Pearson correlation analysis.
The Pearson correlation coefficient obtained was 0.85, confirming positive correlation between the miRNA levels determined by microarray and RT-PCR. Microarray and qRT-PCR
ratios comparing dominant and the largest subordinate follicles
for theca and granulosa cells are shown in Table 3.
miRNA target prediction and pathway analysis. The second
version of DIANA miRPath (41) software, which is based on
miRNAs obtained from miRBase 18 (24), was used to identify
potentially regulated biological pathways in ovarian follicles
for differentially expressed miRNAs. From the total number
Fig. 2. KEGG map visualization of hsa-miR-129-2-3p/bta-miR-129-3p involvement in gonadotropin-releasing hormone (GnRH) signaling pathway. Green, genes
not targeted by this miRNA; yellow, putative targets for this miRNA; red, gene especially important in ovarian follicle physiology.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
expressed miRNAs in theca and 116 in granulosa cells (see
Supplementary Table S1).1 However, to focus our analysis and
discussion we increased the stringency by using a threshold of
P ⬍ 0.01. In theca cells seven miRNAs were more expressed
(P ⬍ 0.01) in dominant than subordinate follicles, while seven
miRNAs were more expressed (P ⬍ 0.01) in subordinate than
dominant follicles (Table 1). In the granulosa cells 17 miRNAs
were more expressed (P ⬍ 0.01) in dominant than subordinate
follicles, whereas expression of 32 miRNAs were more expressed (P ⬍ 0.01) in subordinate than dominant follicles
(Table 2).
To confirm the results obtained from the miRNA microarray, the expression of four miRNAs was analyzed by qRTPCR. In theca cells miRNA hsa-miR-301b/bta-miR-301b
(miR-301b) expression was greater (P ⬍ 0.05) in the dominant
compared with the largest subordinate follicle, while expression levels of hsa-miR-129-2-3p/bta-miR-129-3p (miR-129-2-
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
We next used DIANA miRPath v2.0 software to identify
target genes and pathways for single miRNAs, which were
validated by qRT-PCR. The most affected pathway (P ⫽
1.08e-27) by miR-301b was the Prion diseases pathway in which
the prion protein (PRNP) gene was the only target (Fig. 1). This
miRNA was also associated with TGF-␤ signaling (11 genes,
including TGFBR2 and SMAD5), mechanistic target of rapamycin signaling (8 genes, including IGF1), p-53 signaling
pathway, and a few others (Table 6). miR-129-2-3p was
involved in eight signaling pathways, among them only gonadotropin-releasing hormone (GnRH) signaling pathway (P ⫽
0.0019) is known to be involved in ovarian physiology (Table 7). The
targeted gene in this pathway was MAP3K1 (Fig. 2). Similarly,
MAP3K1 is targeted by miR-18a-5p (Fig. 3) in GnRH signaling pathway, which is one of seven pathways affected by this
miRNA (Table 8). It was the only miRNA for which the
significant threshold was set at P ⬍ 0.05, because there were
no affected pathways for significant threshold set at P ⬍ 0.01.
miR-582-5p affected 14 pathways (Table 9), among them
targets the highest number of genes (24) in P13K-Akt signaling
pathway, including myeloid cell leukemia sequence 1 (MCL1)
gene (Fig. 4).
DISCUSSION
Four differentially expressed miRNAs identified in this
study (miR-301b, miR-129-3p, miR-18a-5p, miR-582-5p)
have not been previously described in regard to ovarian follicle
physiology, and our results suggest their involvement in bovine
ovarian follicle development. Among the most highly expressed miRNAs in theca cells was miR-301b (18.44-fold
higher expression in dominant compared with subordinate
Fig. 3. KEGG map visualization of hsa-miR-18a-5p/bta-miR-18a-5p involvement in GnRH signaling pathway. Green, genes not targeted by this miRNA; yellow,
putative targets for this miRNA; red, gene especially important in ovarian follicle physiology.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
(n ⫽ 63) of differentially expressed miRNAs (P ⬍ 0.01), one
in theca and three in granulosa cells were excluded from the
analysis as they were not available in the database (hsamiRPlus-I181b-2*, hsa-miRPlus-G1140-3p, bta-miR-362-5p,
hsa-miRPlus-C1087). In theca cells, five out of the six miRNAs whose expression was greater in dominant compared with
the largest subordinate follicle were involved in oocyte meiosis, Wnt signaling, TGF-␤ signaling, protein processing in
endoplasmic reticulum, RNA degradation, axon guidance pathways, and pathways in cancer (Table 4). Also in theca cells, the
miRNAs whose expression was greater in subordinate than
dominant follicles were involved in signaling pathways such
as: neurotropin, ErbB, pathways in cancer, insulin, endocytosis, chemokine, regulation of actin cytoskeleton, human T cell
lymphotropic virus type 1 (HTLV-I) infection, and EpsteinBarr virus infection (Table 4). Additionally, in granulosa cells,
for 15 out of 16 miRNAs with greater expression in dominant
than subordinate follicles, the most significantly targeted
pathways that were predicted were neurotropin signaling,
gap junction, endocytosis, phosphatidylinositol-3 kinase
(PI3K)-Akt signaling, MAPK signaling, focal adhesion,
transcriptional misregulation in cancer, pathways in cancer,
vascular smooth muscle contraction, regulation of actin
cytoskeleton, and chemokine signaling pathways (Table 5).
Whereas the miRNAs with greater expression in granulosa
cells of subordinate than dominant follicles 20 out of 29
were involved in following signaling pathways: Wnt,
MAPK, ubiquitin-mediated proteolysis, dopaminergic synapse, neurotropin, axon guidance, mRNA surveillance, pathways in cancer, PI3K-Akt, osteoclast differentiation,
HTLV-I infection, endocytosis, RNA transport, transcriptional misregulation in cancer, and cell cycle (Table 5).
741
742
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
Table 8. Enrichment analysis of miR-18a-5p putative gene targets in KEGG pathways, performed by using DIANA-microT
with significant threshold set at P ⬍ 0.05 (predicted pathways were not found at P ⬍ 0.01)
KEGG Pathway
Gene Name
FDR*
KEGG Pathway ID
Endocytosis
GnRH signaling pathway
Bile secretion
Glycerolipid metabolism
GABAergic synapse
Inositol phosphate metabolism
Dorso-ventral axis formation
SMAP2, IQSEC3, RAB5A, PSD3, RAB11FIP2, CDC42, PARD6B
MAP3K1, CDC42, ADCY4, PRKACB
ABCC2, ABCC3, ADCY4, PRKACB
GK, PNLIPRP3, MBOAT2
GABRA4, ADCY4, PRKACB
INPPL1, IPMK, PIK3C2A
ETV6, NOTCH2
0.010
0.010
0.010
0.020
0.024
0.034
0.034
hsa04144
hsa04912
hsa04976
hsa00561
hsa04727
hsa00562
hsa04320
follicles. This may indicate that this miRNA leads in theca
cells to increase progesterone production and also to inhibition
of androgens that are required for estradiol synthesis in granulosa cells. Thus, these findings suggest that miR-129-3p may
be associated with follicle regression. Another miRNA affecting the MAP3K1 gene in the GnRH signaling pathway is
miR-18a-5p (Fig. 3), whose expression was greater in granulosa cells of dominant compared with subordinate follicles.
Reduced levels of FSH prevent further follicle wave emergence until the dominant follicle undergoes either ovulation, or
regression and atresia (17, 31). Based on the obtained results,
we suggest that miR-18a-5p is an important factor in mediating
the effects of FSH action on granulosa cells. Another identified
miRNA expressed in granulosa cells was miR-582-5p, whose
seed sequence binds to the 3=-UTR of MCL1 gene and reduces
Table 9. Enrichment analysis of miR-582-5p putative gene targets in KEGG pathways, performed by using DIANA-microT
with significant threshold set at P ⬍ 0.05
KEGG Pathway
Vasopressin-regulated water
reabsorption
Glutamatergic synapse
Wnt signaling pathway
Dopaminergic synapse
Endocrine and other factor-regulated
calcium reabsorption
Axon guidance
Transcriptional misregulation in
cancer
Gap junction
MAPK signaling pathway
Melanogenesis
Retrograde endocannabinoid signaling
Pancreatic secretion
Arrhythmogenic right ventricular
cardiomyopathy
PI3K-Akt signaling pathway
Gene Name
FDR*
KEGG Pathway ID
CREB1, ARHGDIB, DCTN2, RAB5A, AQP4, RAB11A,
DYNC1LI1, AQP2, PRKACB
GRM5, GNAI3, GNB1, CPD, PPP3CB, PPP3R1, GNAQ, GRIA4,
HOMER1, GNAO1, GRIK2, CACNA1D, SLC1A2, PRKACB
GSK3B, PPP2R5E, TCF4, VANGL1, PPP2R5A, PRICKLE1, NLK,
FZD4, SENP2, PPP3CB, AXIN2, MAPK8, CSNK1A1, SFRP2,
PPP3R1, CXXC4, WNT2, MAP3K7, TBL1XR1, PRKACB
GSK3B, PPP2R5E, MAPK14, CREB1, GNAI3, GNB1, PPP2R5A,
PPP3CB, MAPK8, PPP2R2A, SCN1A, GNAQ, GRIA4,
GNAO1, CACNA1D, KIF5B, PRKACB, PPP1CB
KL, ATP1B2, ATP1B1, ATP2B1, RAB11A, GNAQ, DNM3,
PRKACB
EFNB2, PLXNA2, GSK3B, PAK7, ROBO2, GNAI3, NCK1,
PTK2, RASA1, EFNB3, PPP3CB, DPYSL5, RHOD, NRP1,
PPP3R1, SEMA3E
CCNT2, TMPRSS2, HPGD, MLLT3, RUNX2, PTK2, PBX3,
ETV1, KDM6A, WHSC1, EWSR1, SIX4, FOXO1
GRM5, GNAI3, LPAR1, GNAQ, GJA1, PRKG1, PRKACB
RASA2, RAP1A, MAPK14, MAP3K1, MAP3K13, TAOK1, NLK,
RASA1, PPP3CB, RASGRP1, MAPK8, FGF9, FGF2, PPP3R1,
NF1, STMN1, CACNB2, CACNA1D, MAP3K7, FGF7,
MAP3K5, PRKACB
GSK3B, TCF4, CREB1, GNAI3, MITF, FZD4, GNAQ, GNAO1,
WNT2, PRKACB
GABRA1, GRM5, MAPK14, GNAI3, GNB1, MAPK8, GNAQ,
GRIA4, GNAO1, CACNA1D, PRKACB
ATP1B2, RAP1A, SLC4A4, ATP1B1, ATP2B1, PLA2G12A,
RAB11A, SLC12A2, GNAQ, ATP2A2
ITGB8, TCF4, DMD, ITGA2, GJA1, ATP2A2, CACNB2, ITGA6,
CACNA1D
PRLR, GSK3B, RBL2, PPP2R5E, ITGB8, PIK3CB, MCL1,
CREB1, GNB1, CDK6, PPP2R5A, PTK2, EIF4E, LPAR1,
PPP2R2A, COL5A1, ITGA2, FGF9, PRKAA1, FGF2, ITGA6,
SGK3, FGF7, COL5A2
7.828949e-10
hsa04962
1.545603e-08
hsa04724
7.05523e-08
hsa04310
7.05523e-08
hsa04728
1.534934e-06
hsa04961
1.697383e-06
hsa04360
0.00034
hsa05202
0.00093
0.00098
hsa04540
hsa04010
0.00098
hsa04916
0.0013
hsa04723
0.0013
hsa04972
0.0013
hsa05412
0.0057
hsa04151
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
follicles), which has been found to affect PRNP gene in the
prion disease pathway (Fig. 1). This agrees with a previous
study in which PRNP mRNA expression and levels of the
protein for PrPC were found to be higher in bovine theca cells
of dominant compared with subordinate follicles, signifying its
role in promotion of dominant follicle development (16).
Another miRNA that was differently expressed in theca cells
was miR-129-2-3p, which our analysis revealed targets
MAP3K1 gene in GnRH signaling pathway (Fig. 2). This
target is involved in two MAPK cascades important for follicular growth and development (JNK and p38MAPK). The
MAPK signaling pathways are associated with regulation of
steroidogenic acute regulatory protein expression and steroidogenesis (27). We have shown that miR-129-2-3p had enhanced expression in subordinate compared with dominant
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
743
its expression (25). Our pathway and gene target analysis
confirmed that this miRNA regulates MCL1 gene in PI3K-Akt
signaling pathway (Fig. 4). Furthermore, miR-582-5p had
higher expression in subordinate compared with dominant
follicles. These results are consistent with other studies in
which an association between growth of the bovine ovarian
dominant follicle and enhanced expression of MCL1 in granulosa cells was reported (13). We speculate that miR-582-5p
plays a key role in follicle development, by decreasing the
expression of MCL1 in granulosa cells of subordinate follicles
undergoing apoptosis.
Several miRNAs have previously been shown to play an
important role in ovarian follicle development (9), and some of
them have been identified in the present study. Expression of
four miRNAs (miR-125b, miR-145, miR-21, miR-34a) have
been reported in cultured bovine theca and granulosa cells,
suggesting their involvement in the follicular-luteal transition
(28). Our research confirmed the expression of these miRNAs
in bovine theca and granulosa cells, but there were no significant differences between dominant and subordinate follicles
with an exception of miR-145. The level of miR-145 was
significantly higher (P ⬍ 0.04) in granulosa cells of dominant
compared with subordinate follicles, but with no significant
differences in theca cells. It is likely, that miR-145 is involved
in both follicular-luteal transition and subordinate follicle regression, while the other miRNAs are only involved in follicular-luteal transition. The results of our study concerning
miR-503 are coherent with other authors who found this
miRNA mostly in theca and luteal cells of ovine preovulatory
follicles (28). We have shown that miR-503 had significantly
higher expression (P ⬍ 0.05) in theca cells of subordinate
compared with dominant follicles, suggesting its function in
programmed cell death of subordinate follicles in cattle. Another miRNA, miR-383, has been reported as a positive regulator of estradiol production in mural granulosa cells (45). Our
miRNA array showed its significantly higher expression in
dominant compared with subordinate follicles in bovine granulosa cells (P ⬍ 0.05). It is well known that increased estradiol
production is considered as a key characteristic of dominant
follicle. Thus, these findings suggest that miR-383 might be an
important factor controlling growth and proliferation of the
dominant follicle.
Using DIANA mirPath v. 2.0 software we predicted key
molecular pathways important in ovarian follicle development,
that are potentially targeted by the miRNAs described in this
paper. These pathways included: oocyte meiosis, Wnt, TGF-␤,
ErbB, and insulin signaling pathways in theca cells and PI3KAkt, MAPK, and Wnt signaling pathways in granulosa cells.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
Fig. 4. KEGG map visualization of hsa-miR-582-5p/bta-miR-582 involvement in phosphatidylinositol-3 kinase (PI3K)-Akt signaling pathway. Green, genes not
targeted by this miRNA; yellow, putative targets for this miRNA; red, gene especially important in ovarian follicle physiology.
744
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
GRANTS
This work was supported by National Science Centre Poland (N N311
324136). The publication was financed by the Faculty of Biology and Animal
Science, Leading National Research Center (KNOW) from the Wrocław
University of Environmental and Life Sciences.
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: A.E.Z.-S. and A.C.O.E. conception and design of
research; A.E.Z.-S., J.A.B., and M.D. performed experiments; A.E.Z.-S. and
P.A.M. analyzed data; A.E.Z.-S., M.G., and T.S. interpreted results of experiments; A.E.Z.-S. prepared figures; A.E.Z.-S. drafted manuscript; A.E.Z.-S.,
J.A.B., and A.C.O.E. edited and revised manuscript; A.E.Z.-S., J.A.B., P.A.M.,
M.G., M.D., T.S., and A.C.O.E. approved final version of manuscript.
REFERENCES
1. Ambros V, Chen X. The regulation of genes and genomes by small
RNAs. Development 134: 1635–1641, 2007.
2. Baley J, Li J. MicroRNAs and ovarian function. J Ovarian Res 5:
1757–2215, 2012.
3. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell
136: 215–233, 2009.
4. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a
practical and powerful approach to multiple testing. J Roy Stat Soc B 57:
289 –300, 1995.
5. Bushati N, Cohen SM. microRNA functions. Annu Rev Cell Dev Bio 23:
175–205, 2007.
6. Canty MJ, Boland MP, Evans AC, Crowe MA. Alterations in follicular
IGFBP mRNA expression and follicular fluid IGFBP concentrations
during the first follicle wave in beef heifers. Anim Reprod Sci 93:
199 –217, 2006.
7. Castañon BI, Stapp AD, Gifford CA, Spicer LJ, Hallford DM, Hernandez Gifford JA. Follicle-stimulating hormone regulation of estradiol
production: possible involvement of WNT2 and ␤-catenin in bovine
granulosa cells. J Anim Sci 90: 3789 –3797, 2012.
8. Datta SR, Brunet A, Greenberg ME. Cellular survival: a play in three
Akts. Genes Dev 15: 2905–2927, 1999.
9. Donadeu FX, Schauer SN, Sontakke SD. Involvement of miRNAs in
ovarian follicular and luteal development. J Endocrinol 215: 323–334,
2012.
10. Dupont J, Reverchon M, Cloix L, Froment P, Ramé C. Involvement of
adipokines, AMPK, PI3K and the PPAR signaling pathways in ovarian
follicle development and cancer. Int J Dev Biol 56: 959 –967, 2012.
11. el-Roeiy A, Chen X, Roberts VJ, LeRoith D, Roberts CT Jr, Yen SS.
Expression of insulin-like growth factor-I (IGF-1) and IGF-II and the
IGF-I, IGF-II, and insulin receptor genes and localization of the gene
products in the human ovary. J Clin Endocrinol Metab 77: 1411–1418,
1993.
12. Erdmann K, Kaulke K, Thomae C, Huebner D, Sergon M, Froehner
M, Wirth MP, Fuessel S. Elevated expression of prostate cancer-associated genes is linked to down-regulation of microRNAs. BMC Cancer
2014, doi:10.1186/1471-2407-14-82.
13. Evans AC, Ireland JL, Winn ME, Lonergan P, Smith GW, Coussens
PM, Ireland JJ. Identification of genes involved in apoptosis and dominant follicle development during follicular waves in cattle. Biol Reprod
70: 1475–1484, 2004.
14. Fatima A, Waters S, O’Boyle P, Seoighe C, Morris DG. Alterations in
hepatic miRNA expression during negative energy balance in postpartum
dairy cattle. BMC Genomics 2014, doi:10.1186/1471-2164-15-28.
15. Fayad T, Lévesque V, Sirois J, Silversides DW, Lussier JG. Gene
expression profiling of differentially expressed genes in granulosa cells of
bovine dominant follicles using suppression subtractive hybridization.
Biol Reprod 70: 523–533, 2004.
16. Forde N, Rogers M, Canty MJ, Lonergan P, Smith GW, Coussens
PM, Ireland JJ, Evans ACO. Association of the prion protein and its
expression with ovarian follicle development in cattle. Mol Reprod Dev
75: 243–249, 2008.
17. Ginther OJ, Bergfelt DR, Kulick LJ, Kot K. Pulsatility of systemic FSH
and LH concentrations during follicular-wave development in cattle.
Theriogenology 50: 507–519, 1998.
18. Ginther OJ, Wiltbank MC, Fricke PM, Gibbons JR, Kot K. Selection
of the dominant follicle in cattle. Biol Reprod 55: 1187–1194, 1996.
19. Hossain MM, Ghanem N, Hoelker M, Rings F, Phatsara C, Tholen E,
Schellander K, Tesfaye D. Identification and characterization of miRNAs
expressed in the bovine ovary. BMC Genomics 10: 443, 2009.
20. Ireland JL, Good TE, Knight PG, Ireland JJ. Alterations in amounts of
different forms of inhibin during follicular atresia. Biol Reprod 50:
1265–1276, 1994.
21. Jamnongjit M, Gill A, Hammes SR. Epidermal growth factor receptor
signaling is required for normal ovarian steroidogenesis and oocyte maturation. Proc Natl Acad Sci USA 102: 16257–16262, 2005.
21a.Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for
integration and interpretation of large-scale molecular data sets. Nucleic
Acids Res 40: D109 –D114, 2012.
22. Knight PG, Glister C. TGF-␤ superfamily members and ovarian follicle
development. Reproduction 132: 191–206, 2006.
23. Kotsuji F, Kubo M, Tominaga T. Effect of interactions between granulosa and thecal cells on meiotic arrest in bovine oocytes. J Reprod Fertil
100: 151–156, 1994.
24. Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39: D152–D157,
2011.
25. Lam LT, Lu X, Zhang H, Lesniewski R, Rosenberg S, Semizarov D.
A microRNA screen to identify modulators of sensitivity to BCL2 inhibitor ABT-263 (Navitoclax). Mol Cancer Ther 9: 2943–2950, 2010.
26. Lin F, Li R, Pan ZX, Zhou B, Yu de B, Wang XG, Ma XS, Han J, Shen
M, Liu HL. miR-26b promotes granulosa cell apoptosis by targeting ATM
during follicular atresia in porcine ovary. PLoS One 7: e38640, 2012.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
These are all intracellular communication networks that involve different signaling pathways, which can interact with
pro- and antiapoptotic factors that appear to determine the fate
of ovarian follicles. It has been reported that signals from theca
cells augment the meiosis-arresting activity of granulosa cells
in bovine oocytes (23). Furthermore, potential role of Wnt
signaling pathway in bovine ovarian steroidogenesis and follicular growth has been indicated (7). TGF-␤ signaling pathway has a significant function in the regulation of ovarian
follicle development at different stages (22). ErbB signaling
pathway is likely to be the main coordinator of LH mechanisms
(21). It has been suggested that EGF may play a significant role
in bovine theca cells steroidogenesis (39). Concerning the
insulin signaling pathway, insulin receptor mRNA has been
found in theca and granulosa cells of human developing antral
follicles (11). Also, in cultured theca cells, insulin has been
shown to stimulate androgen production (29). The PI3K-Akt
signaling pathway is a critical regulator of follicle growth,
differentiation, and survival (8, 10). It has been demonstrated
that regulation of the PI3K-Akt pathway activity is correlated
with bovine dominant follicle selection and development (35).
MAPK signaling pathway regulates cell proliferation, differentiation, and apoptosis. Its signaling protein levels were
greater in bovine dominant compared with subordinate follicles
(35). Moreover, in granulosa cells, the loss of trophic hormonal
support is translated into a decrease of MAPK signaling pathway, and this result in the decreased phosphorylation of the
proapoptotic BCL2-associated agonist of cell death (33).
In conclusion, our study identified miRNAs that are likely to
be regulators of bovine ovarian follicles development through
global regulation of multiple targets and signaling pathways.
Altogether, 14 miRNAs in theca and 49 miRNAs in granulosa
cells were differentially expressed between dominant and subordinate follicles. The predicted targets for these miRNAs were
enriched for pathways involving oocyte meiosis, Wnt, TGF-␤,
ErbB, Insulin, PI3K-Akt, and MAPK signaling pathways.
ROLE OF miRNAs IN OVARIAN FOLLICLE DEVELOPMENT
37. Smyth GK. Limma: linear models for microarray data. In: Bioinformatics
and Computational Biology Solutions using R and Bioconductor, edited by
Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. New York:
Springer, 2005.
38. Smyth GK. Linear models and empirical Bayes for assessing differential
expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article
3, 2004.
39. Spicer LJ, Stewart RE. Interactions among basic fibroblast growth
factor, epidermal growth factor, insulin, and insulin-like growth factor-I
(IGF-I) on cell numbers and steroidogenesis of bovine thecal cells: role of
IGF-I receptors. Biol Reprod 54: 255–263, 1996.
40. Sun J, Wang S, Li C, Ren Y, Wang J. Novel expression profiles of
microRNAs suggest that specific miRNAs regulate gene expression for the
sexual maturation of female Schistosoma japonicum after pairing. Parasit
Vectors 2014, doi:10.1186/1756-3305-7-177.
41. Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M,
Maragkakis M, Paraskevopoulou MD, Prionidis K, Dalamagas T,
Hatzigeorgiou AG. DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res 40: W498 –
W504, 2012.
42. Xu S, Linher-Melville K, Yang BB, Wu D, Li J. Micro-RNA378
(miR-378) regulates ovarian estradiol production by targeting aromatase.
Endocrinology 152: 3941–3951, 2011.
43. Yan G, Zhang L, Fang T, Zhang Q, Wu S, Jiang Y, Sun H, Hu Y.
MicroRNA-145 suppresses mouse granulosa cell proliferation by targeting
activin receptor IB. FEBS Lett 586: 3263–3270, 2012.
44. Yao G, Yin M, Lian J, Tian H, Liu L, Li X, Sun F. MicroRNA-224 is
involved in transforming growth factor-beta-mediated mouse granulosa
cell proliferation and granulosa cell function by targeting Smad4. Mol
Endocrinol 24: 540 –551, 2010.
45. Yin M, Lü M, Yao G, Tian H, Lian J, Liu L, Liang M, Wang Y, Sun
F. Transactivation of microRNA-383 by steroidogenic factor-1 promotes
estradiol release from mouse ovarian granulosa cells by targeting RBMS1.
Mol Endocrinol 26: 1129 –1143, 2012.
Physiol Genomics • doi:10.1152/physiolgenomics.00036.2014 • www.physiolgenomics.org
Downloaded from http://physiolgenomics.physiology.org/ by 10.220.33.1 on June 18, 2017
27. Manna PR, Stocco DM. The role of specific mitogen-activated protein
kinase signaling cascade in the regulation of steroidogenesis. J Signal
Transduct 2011, doi:10.1155/2011/821615.
28. McBride D, Carre W, Sontakke S, Hogg CO, Law AS, Donadeu FX,
Clinton M. Identification of miRNAs associated with the follicular-luteal
transition in the ruminant ovary. Reproduction 144: 221–233, 2012.
29. McGee EA, Sawetawan C, Bird I, Rainey WE, Carr BR. The effect of
insulin and insulin-like growth factors on the expression of steroidogenic
enzymes in a human ovarian thecal-like tumor cell model. Fertil Steril 65:
87–93, 1996.
30. Mihm M, Baker PJ, Fleming LM, Monteiro AM, O’Shaughnessy PJ.
Differentiation of the bovine dominant follicle from the cohort upregulates
mRNA expression for new tissue development genes. Reproduction 135:
253–265, 2008.
31. Mihm M, Bleach EC. Endocrine regulation of ovarian antral follicle
development in cattle. Anim Reprod Sci 78: 217–237, 2003.
32. Miles JR, McDaneld TG, Wiedmann RT, Cushman RA, Echternkamp
SE, Vallet JL, Smith TP. MicroRNA expression profile in bovine
cumulus-oocyte complexes: possible role of let-7 and miR-106a in the
development of bovine oocytes. Anim Reprod Sci 130: 16 –26, 2012.
33. Peter AT, Dhanasekaran N. Apoptosis of granulosa cells: a review on
the role of MAPK-signaling modules. Reprod Domest Anim 38: 209 –213,
2003.
34. Rivera GM, Fortune JE. Proteolysis of insulin-like growth factor binding
proteins -4 and -5 in bovine follicular fluid: implications for ovarian
follicular selection and dominance. Endocrinology 144: 2977–2987, 2003.
35. Ryan KE, Casey SM, Canty MJ, Crowe MA, Martin F, Evans AC. Akt
and Erk signal transduction pathways are early markers of differentiation
in dominant and subordinate ovarian follicles in cattle. Reproduction 133:
617–626, 2007.
36. Schauer SN, Sontakke SD, Watson ED, Esteves CL, Donadeu FX.
Involvement of miRNAs in equine follicle development. Reproduction
146: 273–282, 2013.
745