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Expressed microRNA associated with high rate of egg production in
chicken ovarian follicles
N. Wu
1
, U. Gaur
1
, Q. Zhu, B. Chen, Z. Xu, X. Zhao, M. Yang and D. Li
Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu
610000, China.
Summary MicroRNA (miRNA) is a highly conserved class of small noncoding RNA about 19–24
nucleotides in length that function in a specific manner to post-transcriptionally regulate
gene expression in organisms. Tissue miRNA expression studies have discovered a myriad of
functions for miRNAs in various aspects, but a role for miRNAs in chicken ovarian tissue at
300 days of age has not hitherto been reported. In this study, we performed the first miRNA
analysis of ovarian tissues in chickens with low and high rates of egg production using
high-throughput sequencing. By comparing low rate of egg production chickens with high
rate of egg production chickens, 17 significantly differentially expressed miRNAs were
found (P<0.05), including 11 known and six novel miRNAs. We found that all 11 known
miRNAs were involved mainly in pathways of reproduction regulation, such as steroid
hormone biosynthesis and dopaminergic synapse. Additionally, expression profiling of six
randomly selected differentially regulated miRNAs were validated by quantitative real-time
polymerase chain reaction (RT-qPCR). Some miRNAs, such as gga-miR-34b, gga-miR-34c
and gga-miR-216b, were reported to regulate processes such as proliferation, cell cycle,
apoptosis and metastasis and were expressed differentially in ovaries of chickens with high
rates of egg production, suggesting that these miRNAs have an important role in ovary
development and reproductive management of chicken. Furthermore, we uncovered that a
significantly up-regulated miRNA—gga-miR-200a-3p—is ubiquitous in reproduction-
regulation-related pathways. This miRNA may play a special central role in the
reproductive management of chicken, and needs to be further studied for confirmation.
Keywords egg-laying, illumina sequencing, Luhua chicken, reproduction regulation, RT-
qPCR
Introduction
MicroRNAs (miRNA), which are extensively expressed in a
variety of organisms and tissues, comprise a class of
endogenous small noncoding single-stranded RNA about
19–24 nucleotides (nt) in length (Bartel 2004). Mature
miRNAs from the transcripts of miRNA genes are produced
through processing from the one arm of fold back precursors
(pre-miRNAs) to form approximately 70-nt hairpin sec-
ondary structures (Lee et al. 2004; Kim 2005; Carthew &
Sontheimer 2009). Cellular endogenous miRNA plays
important roles in regulating various biological and meta-
bolic processes, including organogenesis, cell proliferation,
differentiation, development, hematopoiesis, apoptosis,
tumorigenesis and many other cellular processes (Lim et al.
2003; Bartel 2004; Mansfield et al. 2004). According to an
assessment, one miRNA can regulate the expression of
hundreds of mRNAs, and the expression of one mRNA can be
regulated by hundreds of miRNAs (Krek et al. 2005). In other
words, miRNA construct networks of sophisticated regulator
control systems in organisms and play very significant roles.
Genome-wide miRNA expression has been explored in
gonads of cattle (Huang et al. 2011), mice (Ro et al. 2007;
Mishima et al. 2008), sheep (McBride et al. 2012) and pigs (Li
et al. 2011). These studies revealed that miRNAs have a
significantfunction in the development of mammalian gonads.
Some miRNAs have been identified in chicken, and expression
has been reported in several processes, such as embryo
development (Darnell et al. 2006; Glazov et al. 2008; Hicks
Address for correspondence
D. Li, Farm Animal Genetic Resources Exploration and Innovation Key
Laboratory of Sichuan Province, Sichuan Agricultural University,
Chengdu 610000, China.
E-mail: diyanli@sicau.edu.cn
1
These authors contributed equally to this work.
Accepted for publication 01 September 2016
doi: 10.1111/age.12516
1
©2016 Stichting International Foundation for Animal Genetics
et al. 2008, 2010; Bannister et al. 2009; Rathjen et al. 2009;
Wang et al. 2009; Li et al. 2012), immune organ function
(Hicks et al. 2009), germ cell development (Burnside et al.
2008; Lee et al. 2011) and disease (Yu et al. 2008; Tian et al.
2012; Wang et al. 2013). The chicken (Gallus gallus)isan
important agricultural and avian-model species, and it is a
major source of protein worldwide. However, a role for
miRNAs in chicken ovarian development has not hitherto
been reported clearly. The characteristics of ovarian tissue are
highly related to reproductive and economic traits of chicken,
so it is necessary to identify and characterize the miRNA in
ovarian tissue.
In recent years, the development of next-generation
sequencing, which is known as deep sequencing or high-
throughput sequencing technology, has provided a power-
ful, highly reproducible and cost-efficient tool for transcrip-
tomic research (Morozova & Marra 2008). In previous
studies, the catalog of chicken miRNA expression during
embryonic development was characterized using deep
sequencing (Glazov et al. 2008), providing a strong back-
ground of information for analysis of tissue-specific miRNA
signatures. In addition, the genome sequence is available for
chicken, enhancing its use as a model for functional
genomics studies during development. Thus, by means of
miRNA sequencing, we aimed to identify relevant expres-
sive miRNA associated with high rates of egg production
and to extend the repertoire of known miRNAs in chicken
ovarian tissue. Moreover, we can screen suitable miRNAs to
use them as molecular markers in the application of genetic
selection in the breeding programs of chicken.
The Luhua chicken breed (Gallus gallus domesticus), orig-
inally found mainly in Wenshang county of Shandong
province, China, has been recognized as a commercial dual-
purpose egg–meat type chicken (Ruan & Zheng 2011; Hu
et al. 2013). It is a common strain especially selected for its
superior reproductive performance such as high rates of egg
production. In poultry breeding programs, egg number at
300 days of age is usually used as the most valuable indicator
of total egg production potential (Li et al. 2013). In the
present study, we sampled the ovarian tissues of three low-
rate egg production (LP) and three high-rate egg production
(HP) Luhua chickens at age 300 days. Comprehensive
miRNA profiles of ovarian tissue from LP and HP chickens
were generated, and a comparative analysis of miRNA data
was performed. This miRNA data will be very useful in further
understanding the functions of chicken ovarian tissues and
will contribute to the investigation of the regulatory mech-
anism of miRNA in avian species.
Materials and methods
Experimental animals
Eight hundred Luhua chickens from the Experimental
Chicken Farm of Sichuan Agricultural University were used
in this study. A total of 200 LP chickens and 200 HP
chickens were bi-directionally selected according to their
number of eggs at 250 days of age. Six reproductive traits
(body weight at first egg, weight of first egg, age at first egg,
number of eggs at 300 days of age, body weight at 300 days
of age and egg weight at 300 days of age) were recorded for
both groups. The average number of eggs at 300 days of
age (mean SEM) was 111 2.1 and 144 4.3 for LP
and HP chickens respectively. Egg production cycles of these
chickens were also recorded every day. According to their
similar reproductive traits and regular egg production cycle
(about 2 hours prior to ovulation), three LP (B4, B5, B7)
and three HP (A2, A3, A9) chickens (Table S1) were
selected for tissue collection. Experimental procedures were
approved by the Committee on the Care and Use of
Laboratory Animals of the State-level Animal Experimental
Teaching Demonstration Center of Sichuan Agricultural
University (Approval ID: S20141010) and performed in
accordance with the Regulations for the Administration of
Affairs Concerning Experimental Animals (China, 1988) for
animal experiments. All efforts were made to minimize the
suffering of the chickens.
Tissue collection, RNA isolation and small RNA
sequencing
Six chickens were sacrificed by exsanguination at 300 days
of age, and the ovarian tissues, which contained the entire
collection of various-sized follicles (including hierarchical
and all prehierarchical follicles), were collected about
2 hours prior to ovulation. The egg yolks of the follicles
were removed in the process of sample collection. Samples
were quickly stored in RNAlater (Ambion
â
), frozen in liquid
nitrogen and stored at 80 °C until RNA extraction. Total
RNA was isolated from these tissues using an RNeasy Mini
Kit (Qiagen), in accordance with the manufacturer’s
instructions. The quantity and purity of total RNA were
monitored by a Qubit
â
3.0 Fluorometer (Thermo Fisher
Scientific) and formaldehyde–agarose gel electrophoresis
and then stored at 80 °C.
To identify ovarian miRNAs in chickens, complementary
DNA (cDNA) libraries for small RNA from chicken ovarian
tissues were constructed according to published miRNA
cloning protocols (Lagos-Quintana et al. 2001; Lau et al.
2001) and sequenced using an HiSeq2500 (Illumina)
followed by NEXTflexTM Small RNA-seq Kits (BIOO Scientific
Corp.).
Global annotation of miRNA sequences
The raw data, consisting of 17–35 nt sequence adaptors,
first went through a data cleaning process to remove low
quality adaptors, having quality values less than 20, 50and
30primer contaminants, N adaptors, polyA adaptors,
sequences without insert tags and adaptors shorter than
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
Wu et al.2
17 nt and longer than 35 nt. Subsequently, standard
bioinformatic analyses were carried out to align or annotate
the clean adaptors. The clean reads were mapped to the
ensemble chicken genome galGal4 using NCBI MEGABLAST,
and rRNA, tRNA, miscRNA, snRNA and snoRNA were
discarded from the small RNA sequences; the remaining
sequences were again searched against the miRBase 21.0
database of known Gallus gallus miRNA sequences with
zero or one mismatch (http://www.mirbase.org/) were
continued (Ambros et al. 2003; Griffiths-Jones 2004; Grif-
fiths-Jones et al. 2006, 2008). The sequences matching
Gallus gallus miRBase were considered known miRNA
sequences, and the expression of all miRNAs was assayed.
Next, after filtering known miRNA sequences, the remain-
ing sequences were BLAST searched against the Gallus gallus
genome. The sequences matching chicken genome
sequences (except for one to three 50-or3
0-end nt) were
used to predict novel miRNAs by MIRDEEP2 (https://
www.mdcberlin.de/8551903/en/research/research_teams/
systems_biology_of_gene_regulatory_elements/projects/miR
Deep) using default parameters. These sequences were
considered as a potential novel miRNA, and expression of all
miRNAs was assayed.
Hierarchical cluster analysis of differentially expressed
miRNAs
Differential expression for known and novel miRNAs
were analyzed using EDGER (http://www.bioconductor.org/
packages/release/bioc/html/edgeR.html). Reads per million
miRNA mapped values were used to represent miRNA
expression levels. P-values were calculated using right-
tailed Fisher’s exact test. P<0.05 and |log
2
fold change|≥1
were used to screen differentially expressed miRNAs. A
hierarchical cluster analysis was performed for known and
novel miRNAs with similar expression patterns (Zhang et al.
2009).
MiRNA target gene prediction and functional
annotation
The miRNA targets were predicted by analyzing the
putative miRNA binding sites in the libraries. The miRNA
target prediction software MIRDB (http://mirdb.org/miRDB/
index.html) (Wong & Wang 2014) was used to predict the
binding sites of differentially expressed miRNA. The Tar-
getScan principle (http://www.targetscan.org/) was also
applied in the prediction procedures. The intersection of the
two prediction programs was selected for this study.
The BLAST2GO program was used to conduct gene ontology
(GO) annotations and GO functional classifications (Conesa
et al. 2005) of these predicted miRNA target genes. In GO
terms, P-value ≤0.001 was used to identify the significantly
enriched GO terms. These genes were also submitted to the
KEGG (Kyoto Encyclopedia of Genes and Genomes) database
for enrichment analyses. The P-value denotes the signifi-
cance of the pathway correlated to the conditions. The
lower the P-value, the more significant the metabolism
pathway (P-value cut-off was 0.05).
Validation of miRNA expression by real-time PCR (RT-
qPCR)
To validate the reliability of the Illumina analysis, we
randomly tested the expression of eight miRNAs, including
six differentially expressed miRNAs and two similarly
expressed miRNAs. Reverse transcription (RT) real-time
PCR was used to quantify the expression of eight mature
miRNAs. The RT-qPCR primers were designed using PRIMER
5.0 (http://downloads.fyxm.net/Primer-Premier-101178.
html) and are listed in Table S2 (miRNA-specific primers
were synthesized by Shanghai Biological Technology Co.
and universal primers were provided by miRcute miRNA
qPCR Detection kit, Aidlab).
Briefly, the total RNA of each sample was reverse-
transcribed with miRNA-specific RT primers using the First-
strand cDNA Synthesis Kit (Thermo Scientific Fermentas).
First, 2 llof109poly(A) polymerase buffer, 2 llof109
rATP Solution, 0.4 ll of E. coli Poly(A) Polymerase (5 U/ll)
and 2 ll of total RNA (10 pgll
1
~1.5 lgll
1
) were added
into ice-cold RNase-Free reaction tubes and supplemented
with RNase-Free ddH
2
O to a total volume of 20 ll. After
brief centrifugation, the mixture was incubated for 50 min
at 37 °C. Second, 3 ll of the Poly (A) reaction mixture
obtained in the first step was added to a solution containing
2llof25lmol RT Primer, 4 llof59RT Buffer Reaction
Mix, 0.8 ll of TUREscript H
RTase (200 U/ll) and 10.2 ll
of RNase-Free ddH
2
O. After brief centrifugation, the reac-
tion solutions were incubated at 42 °C for 50 min, at 70 °C
for 15 min and then held at 20 °C.
The expression of mature miRNAs was detected using the
SYBR green method. RT-qPCR was performed in a 96-well
plate using the Bio-Rad iQ5 Real-time PCR Detection
System, according to the protocol. In a 20-ll reaction
mixture, 1.0 ll of cDNA was used as a template, with 10 ll
of SYBR Select Master Mix (Applied Biosystems), 0.4 llof
specific forward primer and 0.4 ll of universal primer, with
the following program: 94 °C for 3 min, followed by 42
cycles of 94 °C for 20 s and the optimal temperature for
40 s. 5.8S rRNA, which has relatively stable expression in
most tissues, was used as an endogenous control (Elela &
Nazar 1997), and the expression level of 5.8S rRNA was
used to normalize the RT-qPCR results for each miRNA. All
reactions were run in three technical replicates, including
negative controls without template. Fold changes of miRNA
expression were calculated using the 2
DDCt
method (versus
5.8S rRNA) (Schmittgen & Livak 2008). All of the data
were expressed as the mean standard deviation, and a
statistical analysis using Student’s t-tests was performed
with SPSS 16.0 (SPSS Inc.).
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
Ovarian microRNA transcriptome of chicken 3
Accession code
The Illumina HiSeq 2500 sequencing data for the Luhua
chicken ovarian miRNA sequences has been deposited in
the NCBI Sequence Read Archive (SRA, http://
www.ncbi.nlm.nih.gov/Traces/sra) with accession no.
SRP062266.
Results
Overview of miRNA sequencing
A total of 30.20 million and 30.37 million raw reads were
obtained from ovarian tissues of LP and HP chickens
respectively. After filtering the low-quality sequences,
28 501 792 and 28 004 558 clean reads from the HP
(93.83% clean) and LP (92.72% clean) chicken ovary
libraries respectively were selected for further analysis
(Table S3). The total and unique read length distributions
of LP and HP chickens are shown in Fig. S1. Among the
selected reads, the size distribution of small RNAs for
sequencing was similar in six samples. The majority of these
total small RNAs ranged from 20–24 nt, and unique reads
were distributed in mainly 21–23 nt; there was no obvious
difference between the LP and HP chickens. The results are
consistent with the typical size range of small RNAs and
indicated that the clean reads included a large number of
miRNA sequences.
Annotation of small RNA sequences
Small RNA sequences identified by Illumina small RNA deep
sequencing were compared with known mature miRNAs
and precursors in miRBase 21.0. The unique and all small
RNAs were annotated as miRNA, snRNA, tRNA, rRNA,
snoRNA, miscRNA (repeat and polII-transcribed) and
unannotated for the LP and HP chickens (Table S4). The
HP chickens had a larger number of small RNAs and unique
small RNAs than did the LP chickens. Most small RNAs in
both groups were miRNAs (82%) and unannotated (16%)
for all small RNAs, whereas for unique small RNAs, HP
chickens had the greatest proportion of miRNAs (41%) and
LP chickens had only 33% of miRNAs as the second major
class (Fig. S2). This revealed that HP chickens produce more
unique miRNAs, but the difference is not significant
(P>0.05). Proportions of the remaining categories of small
RNAs, including tRNA, rRNA, snRNA, miscRNA or
snoRNA, were relatively lower (<1.2%).
Expression of known and novel miRNAs in chicken
ovaries
In the present study, 562 known miRNAs were identified in
all samples including 507 in HP and 496 in LP chickens
(Table S5). Among the 562 sequenced mature miRNAs,
441 (78.5%) unique miRNAs were expressed in all LP and
HP chickens; however, 66 (11.7%) and 55 (12.5%) were
specifically expressed in HP and LP chickens respectively. In
the miRNA expression profile, the read numbers of the top
10 miRNAs accounted for 81.93% of the total reads from
LP chickens and 78.3% of the total reads from HP chickens.
The miRNA expression profile revealed that most miRNAs
were expressed by a small portion of miRNA genes.
Interestingly, we found that the miRNAs with the 19
highest expression levels were the same for both groups.
Among them, miR-99a-5p and gga-miR-26a-5p displayed
more than 7 500 000 reads and had the highest expression
level, followed by gga-miR-199-5p and gga-miR-10a-5p,
which displayed about 2 500 000 reads each. Some
miRNAs (such as gga-miR-222b-3p and gga-miR-1729-
5p) displayed fewer than about 1 000 reads, thereby
indicating that expression varied significantly among
different miRNAs; this result is consistent with previous
studies (Gu et al. 2014).
A total of 470 novel miRNAs were predicted in this study,
and 389 and 384 novel miRNAs were identified in HP and
LP chickens respectively (Table S6). Among the 470
predicted novel miRNAs, 303 (64.5%) unique miRNAs
were expressed in both groups; however, 86 (18.3%) and
55 (17.2%) were specifically expressed in HP and LP
chickens respectively. The novel miRNA expression profile
also revealed that most novel miRNAs were expressed by a
small portion of miRNA genes. Moreover, we found that the
sequencing frequencies of novel miRNAs were much lower
compared to those of known miRNAs. The same expression
pattern has also been reported in other species (Chi et al.
2011; Xu et al. 2013), which suggests that novel miRNAs
are usually weakly expressed whereas known miRNA genes
are highly expressed.
Hierarchical cluster analysis of differentially expressed
miRNAs
We first performed a correlation analysis to assess the
variance of miRNA expression across replicates. All samples
showed high reproducibility in the same group, with
Pearson correlation coefficients >0.85 (Fig. 1). Known and
novel miRNAs were compared between the two groups to
identify differentially expressed miRNAs. The results of the
analysis demonstrated that there were 17 significantly
differentially expressed miRNAs, which were divided into
two categories, the upper part comprising eight up-
regulated miRNAs and the lower half nine down-regulated
miRNAs in HP chickens (Table 1 & Fig. 2). In the cluster
analysis, miRNAs that showed similar patterns of differen-
tial expression in different sample pairs were clustered
together. Based on miRBase 21.0, these 17 miRNAs
comprised five clusters, of which one cluster contained
eight miRNAs, one cluster contained five miRNAs, one
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
Wu et al.4
cluster contained two miRNAs and two clusters contained
one miRNA (Fig. S3).
Target gene prediction and gene functional annotation
Target prediction is an important way to determine the
functions of miRNAs. We predicted a total of 1305 target
genes among the differentially expressed miRNAs
(Table S7). To probe the biological roles of the differentially
expressed miRNAs, all of the predicted targets in this study
were mapped to terms in the GO and KEGG databases. A
total of 128 significantly enriched GO terms and 18 KEGG
pathways regulated by 11 differentially expressed known
miRNAs were identified (Table S8). Among the top 30
significantly GO terms for biological process, most target
genes were associated with biological regulation, single-
organism cellular process, single-organism process and
regulation of metabolic process (Fig. 3). For cellular
component, target genes were significantly associated with
intracellular part. For molecular function, target genes
were significantly related to protein binding and ion
binding. Results of the KEGG analysis showed that these
target genes were involved mainly in endocytosis, miRNAs
in cancer, glycerol phospholipid metabolism, thyroid hor-
mone signaling pathway and ubiquitin mediated proteol-
ysis pathways (Fig. 4). Involvement in cancer pathways
suggest that the differentially expressed miRNAs play a
regulatory role in cell proliferation and cell cycle
progression. Notably, a specific enrichment of genes was
found in some reproduction regulation pathways, such as
steroid hormone biosynthesis, dopaminergic synapse,
GnRH signaling pathways, oxytocin signaling pathway,
oocyte meiosis, calcium signaling pathways, progesterone-
mediated oocyte maturation, endocrine and other factor-
regulated calcium reabsorption and MAPK signaling
pathway (Table S9).
A_ovary
B_ovary
A_ovary
B_ovary
A
3_ovary
A
2_ovary
A
9_ovary
B4_ovary
B5_ovary
B7_ovary
A3_ovar
y
A2_ovar
y
A9_ovar
y
B4_ovary
B5_ovary
B7_ovary
0.7 0.8 0.9 1
Value
Color key
Figure 1 Correlation analysis of miRNA
expression across samples of LP and HP
chickens.
Table 1 miRNAs significantly differentially expressed between low-
and high-rate egg production chickens.
miRNA logFC P-value Up-/down-regulated
gga-miR-34b-5p 3.73965 0.00003 Up
gga-miR-34c-5p 2.56597 0.00021 Up
gga-miR-34b-3p 1.74087 0.02398 Up
gga-novel-18-star 4.34580 0.03305 Up
gga-miR-34c-3p 1.93322 0.03626 Up
gga-miR-200a-3p 1.29779 0.03646 Up
gga-novel-31-mature 2.27375 0.03687 Up
gga-miR-1641 2.61869 0.04540 Up
gga-miR-1744-3p 3.16434 0.00086 Down
gga-novel-280-mature 2.55983 0.00944 Down
gga-miR-1655-5p 1.85425 0.02031 Down
gga-novel-73-star 2.66111 0.02283 Down
gga-miR-216b 2.75966 0.02540 Down
gga-miR-1734 3.19824 0.03786 Down
gga-novel-79-mature 4.02767 0.03809 Down
gga-novel-81-mature 4.02779 0.03823 Down
gga-miR-7465-3p 1.71936 0.04816 Down
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
Ovarian microRNA transcriptome of chicken 5
Verification of miRNA and targeted gene expression
RT-qPCR detection assays were used to confirm the
expression of certain differentially expressed miRNAs in
the LP and HP chickens. The expression of eight randomly
selected miRNAs was verified by real-time PCR (Table 2 &
Fig. 5). The expression of gga-miR-34b-3p, gga-miR-34c-3p
and gga-miR-200a-3p were up-regulated, whereas gga-
miR-1744-3p, gga-miR-216b and gga-miR-1655-5p were
down-regulated and gga-miR-99a-5p and gga-miR-26a-5p
showed no significant difference, which was in agreement
with the high-throughput sequencing results (Table 1). The
discrepancies with respect to ratio may be attributed to the
essentially different algorithms and sensitivities between the
two techniques.
We further investigated the potential roles of the 17
miRNAs in regulating targeted gene expression using RNA-
seq. Thirty-nine predicted targets of 10 miRNAs showed
evidence of differential expression between HP and LP
chickens (Fig. S4). Among them, most genes have been
demonstrated to play important roles in regulating organo-
genesis, development, tumorigenesis and many other pro-
cesses (Moreau et al. 1997; Sarbassov et al. 2004; Kuo et al.
2011; Kanda et al. 2016). Our results suggest that these
genes are also associated with reproductive traits of hens.
These results provide invaluable insights into candidate genes
for reproductive traits and selective breeding of chicken.
Discussion
The miRNA sequences we identified were generally close to
22 nt in length, in agreement with previous reports (Yuan
et al. 2014; Li et al. 2015), and annotation of RNA
0 5 10 15
−4 −2 0 2 4
MA plot
logCounts
logFC
−4 −2 0 2 4
01234
Volcano plot
logFC
−log10 (P value)
Figure 2 Scatter plot of the high-throughput
sequencing data. The high-throughput
sequencing data (differentially expressed
miRNAs) are graphed on the scatter plot to
visualize variations in miRNA expression
between HP and LP chickens. The graph
reflects the fold change value (HP/LP) distri-
bution in the differentially expressed miRNA
numbers. In both plots, red dots represent the
differentially expression miRNAs, and black
dots represent the similarly expressed miRNAs.
GO classification
Target gene number
0
200
400
600
800
1000
1200
Small GTPase mediated signal transduction
Intracellular signal transduction
Ras protein signal transduction
Regulation of response to stimulus
Regulation of signal transduction
Biological regulation
Cellular protein modification process
Protein modification process
Macromolecule modification
Single−organism cellular process
GTP metabolic process
Guanosine−containing compound metabolic process
Phosphorus metabolic process
Phosphate−containing compound metabolic process
GTP catabolic process
Single−organism process
Guanosine−containing compound catabolic process
Regulation of small GTPase mediated signal transduction
Regulation of catabolic process
Regulation of GTP catabolic process
Regulation of Rho protein signal transduction
Rho protein signal transduction
Regulation of Ras protein signal transduction
Regulation of metabolic process
Regulation of cell communication
Regulation of GTPase activity
Intracellular
Intracellular part
Protein binding
Ion binding
Biological_process
Cellular_component
Molecular_function
Figure 3 Selected top 30 GO categories of predicted target genes regulated by the differentially expressed miRNAs.
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
Wu et al.6
distribution showed that the clean reads included a myriad
of miRNA sequences. All of the above results suggest that
the major clean reads mapped to known miRNAs in
miRBase were highly enriched and that the deep sequenc-
ing data are representative of the miRNA expression profile
of ovarian tissues and would be reliable for subsequent
analyses and prediction of novel miRNAs.
In the ovary libraries, gga-miR-99a-5p and gga-miR-26a-
5p were the two most frequently sequenced miRNAs
(>7 500 000 reads). Previous studies demonstrated that
mir-99a and mir-99b can inhibit proliferation of c-Src-
transformed cells and prostate cancer cells by targeting
mTOR (Oneyama et al. 2011; Sun et al. 2011). They were
also identified as two novel target miRNA genes of
transforming growth factor-b (TGF-b) and played important
roles during TGF-b-induced epithelial to mesenchymal
transition (EMT) of NMUMG cells (Turcatel et al. 2012).
TGF-bis a secreted cytokine that regulates a variety of
processes in development and cancer including EMT (Bierie
& Moses 2006a,b). EMT is a key process during embryonic
development and disease development and progression.
miR-26a is reported to have anti-apoptotic effects on many
cancers (Garzon et al. 2006; Saito & Jones 2006; Zhang
et al. 2011). It was also found to play a role in normal tissue
growth and development and to have an impact on cell
proliferation and differentiation (Luzi et al. 2008; Wong &
Tellam 2008). One study showed that miR-26a regulates
osteoblast cell growth and differentiation in human adipose
tissue-derived stem cells (Luzi et al. 2008). Among other
miRNAs, we found that the let-7 miRNA family was
another abundant cluster with let-7a-5p being the most
abundantly expressed miRNA. The let-7 miRNA family was
also found to be abundantly expressed in ovary and oocyte
of bovines (Tripurani et al. 2010; Huang et al. 2011b; Miles
et al. 2012), as well as in murine ovaries and testis (Reid
et al. 2008). Furthermore, gga-miR-10a-5p, gga-miR-146c-
5p, gga-miR-21-5p, gga-miR-148a-3p, gga-miR-126-3p
and gga-miR-30d were abundant in our sequencing
libraries, as has been shown in other animal gonads
(Mishima et al. 2008; Md Munir et al. 2009; Tripurani et al.
2010; Kang et al. 2013). The significant biological func-
tions of these miRNAs imply that they have important roles
in the female reproductive physiology of chicken.
Among the significantly differentially expressed miRNAs,
gga-miR-34b and gga-miR-34c (both including 3p and 5p)
exhibited a significant increase in the HP ovary compared with
Alanine
Bacterial invasion of epithelial cells
Circadian rhythm
Circadian rhythm − fly
Endocytosis
Epithelial cell signaling in Helicobacter pylori infection
ErbB signaling pathway
Fc gamma R−mediated phagocytosis
Glycerophospholipid metabolism
Inositol phosphate metabolism
Lysine degradation
MicroRNAs in cancer
Mucin type O−glycan biosynthesis
Phosphatidylinositol signaling system
SNARE interactions in vesicular transport
Thyroid hormone signaling pathway
Ubiquitin mediated proteolysis
Vasopressin−regulated water reabsorption
0 5 10 15 20 25
Gene number
Pathway term
0.01
0.02
0.03
0.04
P value
Figure 4 Top 18 pathways of predicted target genes regulated by the differentially expressed miRNAs.
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
Ovarian microRNA transcriptome of chicken 7
the LP ovary. Previous studies identified the mir-34 family as a
p53 target and a potential tumor suppressor for regulating
processes such as proliferation, cell cycle, apoptosis and
metastasis (Bommer et al. 2007; He et al. 2007a,b; Hermeking
2009). The expression of miR-34a, b and c appears to be
correlated with p53 and was found in over 50% of human
cancers, including pancreatic cancer (Ji et al. 2009), lung
adenocarcinoma (Okada et al. 2014), breast cancer (Kato et al.
2009), gastric cancer (Ji et al. 2008), ovarian cancer (Corney
et al. 2010) and so on. Some studies also revealed that, in
Drosophila, miR-34 loss triggers a gene profile of accelerated
brain aging, late-onset brain degeneration and a catastrophic
decline in survival; miR-34 upregulation extends median
lifespan and mitigates neurodegeneration induced by human
pathogenic poly-glutamine disease protein; and miR-34 can
regulate age-associated events and long-term brain integrity to
modulate aging and neurodegeneration (Liu et al. 2012). In the
present study, gga-miR-34b and gga-miR-34c both had a
significant increase in HP ovary compared with LP ovary, and
gga-miR-34b-5p had the highest level—a 13.36-fold increase
—in HP ovary, implying that gga-miR-34 plays an important
role in reproductive management in hens.
Previous studies reported that miR-216b attenuated
nasopharyngeal carcinoma cell proliferation, invasion and
tumor growth in nude mice and that it mediated its tumor
suppressor function, at least in part, by suppressing
downstream pathways of KRAS, such as in the PI3K-AKT
and MEK-ERK pathways (Deng et al. 2011). Other studies
also showed that miR-216b inhibits cell proliferation,
migration and invasion of hepatocellular carcinoma by
regulating insulin-like growth factor 2 mRNA-binding
protein 2 and that it is regulated by the hepatitis B virus
x protein (Liu et al. 2015). In the present study, gga-miR-
216b was down-regulated 6.77-fold in HP ovary, sug-
gesting that the suppressing expression of gga-miR-216b
may be beneficial for the improvement of egg laying in hens.
Among the KEGG pathways, some pathways associated
with endocytosis, cancer, Fc gamma R-mediated phagocy-
tosis, snare interactions in vesicular transport, mucin type
o-glycan biosynthesis, lysine degradation, ubiquitin
mediated proteolysis, alanine and some signal transduction
pathways, such as the phosphatidylinositol signaling sys-
tem, thyroid hormone signaling pathway and ErbB signal-
ing pathway, were all significantly enriched. This indicates
a role of the differentially expressed miRNAs in the
regulation of cell motility, cell proliferation, cell nutrition,
nervous system development and function, communication
between cells and the extracellular matrix. In addition, a
small number of genes involved in the epithelial cell
signaling pathways in Helicobacter pylori infection and
bacterial invasion of epithelial cells suggests that the hens
were involved in a stage of immune regulation.
We also found that specific enrichment of predicted
targeted genes was involved in some reproduction-related
pathways, such as steroid hormone biosynthesis, dopamin-
ergic synapse and GnRH signaling pathways. Some of these
genes have been demonstrated to play important roles in
ovary development and reproductive management of hens;
for example, FSHB produces a pituitary glycoprotein
hormone, FSH, that plays a key role in the reproductive
system of chicken, including steroidogenesis, folliculogene-
sis and follicular maturation (Choi et al. 2005). Moreover,
we observed that all differentially expressed known miRNAs
were involved in reproduction-related pathways, and bio-
logical functions of most miRNAs have not been reported.
This provides us with a guideline to explore their unknown
roles in reproductive management of hens. Furthermore, we
uncovered a miRNA—gga-miR-200a-3p—that is ubiqui-
tous in most reproduction-regulation-related pathways. The
miR-200a family was reported to play a critical role in
tumor progression and metastasis, specifically in the main-
tenance of the epithelial phenotype by targeting ZEB1, SIP1
and SIRT1, thus preventing the silencing of E-cadherin
Table 2 Expression profile of eight randomly selected miRNAs.
miRNA
Fold change (high
egg-laying/low
egg-laying) P-value
RNA-Seq RT-qPCR RNA-Seq RT-qPCR
gga-miR-1744-3p 0.11 0.16 0.0009 0.0148
gga-miR-216b 0.15 0.03 0.0254 0.0002
gga-miR-1655-5p 0.28 0.09 0.0203 0.0049
gga-miR-99a-5p 0.88 1.06 0.6960 0.3723
gga-miR-26a-5p 0.86 0.78 0.6440 0.2766
gga-miR-34b-3p 3.34 3.49 0.0240 0.0017
gga-miR-34c-3p 3.81 3.75 0.0363 0.0026
gga-miR-200a-3p 2.46 2.34 0.0365 0.0027
Relative expression
gga-miR-1744-3p
gga-miR-216b
gga-miR-1655-5p
gga-miR-99a-5p
gga-miR-26a-5p
gga-miR-34b-3p
gga-miR-34c-3p
gga-miR-200a-3p
0
1
2
3
4
5
LP
HP
**
***
*
*
ns ns
* *
*
*
Figure 5 Validation of the miRNA expression profile by RT-qPCR.
Relative expression of miRNAs was calculated according to the 2
DDCt
method using 5.8S rRNA as an internal reference RNA. The error bar
shows the standard deviation. *P<0.05, **P<0.01, ***P<0.001.
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
Wu et al.8
(Bracken et al. 2008; Eades et al. 2011). In several mes-
enchymal phenotype breast cancer cell lines, the miR-200a
family was found to be down-regulated (Gregory et al.
2008) and enforced expression of miR-200a family mem-
bers prevented TGF-b-induced epithelial to mesenchymal
transition (Eades et al. 2011). In breast cancer, in addition
to targeting ZEB1 and SIP1, the miR-200 family has been
shown to target phospholipase C-c1 and BMI1, reducing
EGF-driven motility and cancer stem cell self-renewal
respectively (Shimono et al. 2009; Uhlmann et al. 2010).
In the present study, gga-miR-200a-3p exhibited a signif-
icant 2.46-fold increase in HP ovary, implying that the
miRNA may play a special essential role in ovary develop-
ment and reproductive management of hens.
In previous work, genomic and transcriptomic studies
have identified that some genes and miRNAs are related to
egg production performance. Kang et al. (2009) revealed 18
known and eight unknown differentially expressed genes
detected in ovarian tissues from the pre-laying to the egg-
laying stages (Kang et al. 2009). Kang et al. (2013) iden-
tified 202 known miRNAs, 93 of which were found to be
significantly differentially expressed in sexually immature
and mature chicken ovaries. Luan et al. (2014) presented
the transcriptomic profiles of ovarian tissue from Huoyan
geese during the ceased and laying periods using RNA-Seq.
In the present study, we performed the first miRNA analysis
of low- and high-rate of egg production chicken ovarian
tissues using high-throughput sequencing. Compared with
other studies, we found some new significantly differentially
expressed ovarian miRNAs, such as gga-miR-1744-3p, gga-
miR-1655-5p, gga-miR-1734 and gga-miR-7465-3p, in the
high egg-laying chickens. These newly identified miRNAs
will be an important guideline for the future research.
Conflict of interest
All authors declared no conflict of interest exists.
Acknowledgements
This work was supported by the Program from Sichuan
Agricultural University (02920400), the National Natural
Science Foundation of China (NSFC31402063) and the
Sichuan Provincial Department of Science and Technology
Program (2015JQO023).
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Ovarian microRNA transcriptome of chicken 11
Supporting information
Additional supporting information may be found online in
the supporting information tab for this article:
Figure S1 Length distribution of total small RNA in LP and
HP chicken ovary libraries.
Figure S2 Small RNAs percentages of (A) HP and (B) LP
chickens.
Figure S3 Cluster plots of the 17 differentially expressed
miRNAs.
Figure S4 Thirty-nine differentially expressed predicted
targets of 10 candidate miRNAs.
Table S1 Main reproductive traits of the selected LP and HP
samples.
Table S2 Primer sequences for real-time PCR.
Table S3 Overview of miRNA-seq data of all samples.
Table S4 Identities of various small RNA sequences.
Table S5 Expression profile of known miRNAs in ovarian
tissues of low and high rate of egg production chickens.
Table S6 Novel miRNAs expressed in ovarian tissues of low
and high rate of egg production chickens.
Table S7 Prediction of the differentially expressed miRNA
targets.
Table S8 GO and KEGG pathway annotations for the
miRNA targets.
Table S9 miRNAs and their predicted target genes involved
in the reproduction regulation process.
©2016 Stichting International Foundation for Animal Genetics, doi: 10.1111/age.12516
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