MicroRNA expression profiles from eggs of different qualities associated with post-ovulatory ageing in rainbow trout (Oncorhynchus mykiss)
Ma et al. BMC Genomics
MicroRNA expression profiles from eggs of different qualities associated with post-ovulatory ageing in rainbow trout (Oncorhynchus mykiss)
Hao Ma 0
Gregory M Weber 2
Mark A Hostuttler 2
Hairong Wei 1
Lei Wang 0
Jianbo Yao 0
0 Division of Animal and Nutritional Sciences, West Virginia University , Morgantown, WV 26506 , USA
1 School of Forest Resources and Environmental Science, Michigan Technological University , Houghton, MI 49931 , USA
2 National Center for Cool and Cold Water Aquaculture, USDA/ARS , Kearneysville, WV 25430 , USA
Background: Egg quality is an important aspect in rainbow trout farming. Post-ovulatory aging is one of the most important factors affecting egg quality. MicroRNAs (miRNAs) are the major regulators in various biological processes and their expression profiles could serve as reliable biomarkers for various pathological and physiological conditions. The objective of this study was to identify miRNAs that are associated with egg qualities in rainbow trout using post-ovulatory aged eggs. Results: Egg samples from females on day 1, day 7, and day 14 post-ovulation (D1PO, D7PO and D14PO), which had the fertilization rates of 91.8%, 73.4% and less than 50%, respectively, were collected and small RNAs isolated from these samples were subjected to deep sequencing using the Illumina platform. The massive sequencing produced 27,342,477, 26,910,438 and 29,185,371 reads from the libraries of D1PO, D7PO and D14PO eggs, respectively. A three-way comparison of the miRNAs indicated that the egg samples shared 392 known and 236 novel miRNAs, and a total of 414, 481, and 470 known and 243, 298, and 296 novel miRNAs were identified from D1PO, D7PO and D14PO eggs, respectively. Four known miRNAs (omy-miR-193b-3p, omy-miR-203c-3p, omy-miR-499-5p and omy-miR-7550-3p) and two novel miRNAs (omy-miR-nov-95-5p and omy-miR-nov-112-5p) showed significantly higher expression in D1PO eggs relative to D14PO eggs as revealed by both deep sequencing and real time quantitative PCR analysis. GO analysis of the predicted target genes of these differentially expressed miRNAs revealed significantly enriched GO terms that are related to stress response, cell death, DNA damage, ATP generation, signal transduction and transcription regulation. Conclusions: Results indicate that post-ovulatory ageing affects miRNA expression profiles in rainbow trout eggs, which can in turn impact egg quality. Further characterization of the differentially expressed miRNAs and their target genes may provide valuable information on the role of these miRNAs in controlling egg quality, and ultimately lead to the development of biomarkers for prediction of egg quality in rainbow trout.
microRNA; Egg quality; Post ovulation; Rainbow trout
Fish egg quality is defined as the capability of an egg to
become fertilized and subsequently develop into a normal
embryo or the probability of eggs to exhibit low
mortalities at fertilization, eyeing, hatching, and first feeding .
The production of high quality eggs is a major objective of
the aquaculture industry, as egg quality not only affects
fertilization rate, but also is an important attribute of
robust embryonic development [2,3]. However, visible
differences between good and bad eggs at oviposition is not
usually conspicuous in rainbow trout, and therefore, the
inclusion of eggs from individual females with poor egg
quality into mass incubation units not only results in
unexpected losses in egg production, but also problems
associated with the removal of dead eggs and embryos
after fertilization and fungi infection in the hatchery .
Therefore, enabling evaluation of the egg quality before
fertilization is highly desirable in aquaculture production.
In teleost fish, a mature egg is developed through
multiple phases, including primary oocyte growth, secondary
growth including the cortical alveolus stage and
vitellogenesis, follicle maturation and ovulation [5,6]. The
coordinated multiple developmental stages can be affected
by many genetic, biological, and environmental factors
. It has been reported that the quality of rainbow trout
eggs is dependent not only on the genetic characteristics
of parents , but also the age of female , and are
susceptible to environmental influences, such as the diet of
brood fish [8-12], stress [13-15], photoperiod , and the
physiochemical conditions of the water . All of these
factors make egg quality highly variable and difficult to
control [18,19]. As the ovulated eggs in reared rainbow
trout do not usually oviposit naturally, post-ovulatory
aging of the eggs is widely accepted as a common
determinant for egg quality [20-22].
The importance to distinguish good and bad quality
eggs before fertilization has driven studies on the
identification of markers associated with egg quality in rainbow
trout. Wojtczak and coworkers found that very poor
quality eggs turn water turbid . Egg survival rate has also
been associated with aspects of egg composition . The
total amount of water imbibed after 30 minute incubation
has been recognized as an indicator of egg quality . In
addition, molecular markers that are potential indicators
of egg quality have also been reported. Such markers
include the maternally-derived IGF-I and IGF-II , and
LVII fragments as well as other proteins identified from
the coelomic fluid . Many specific genes that are
potentially involved in the regulation of oocyte maturation,
egg developmental potential, and embryo survival were
identified by microarray and quantitative real time PCR
(RT-qPCR) analyses [3,27,28]. Although the above studies
have attempted to address the critical factors responsible
for the observed variability of egg quality, the molecular
mechanisms underpinning the regulation of egg quality in
rainbow trout remain largely elusive.
Recent advances in epigenetic research have
demonstrated that the evolutionarily conserved microRNAs
(miRNAs) play important roles in differentiation and
maturation of various cell types [29,30]. In zebrafish,
mutant embryos lacking mature miRNAs had severe
deformity during embryogenesis [31-34]. In mouse,
although the miRNA functions are suppressed during
oocyte maturation, the maternal miRNAs are critical to
normal embryonic development [35-38]. In addition, many
miRNAs have been shown to play roles in programmed
cell death [38-42]. A comprehensive review of miRNAs in
teleost fish development, reproduction and response to
environmental stimuli was published recently . In order
to identify the miRNAs that might play important roles in
oocyte and embryonic development in rainbow trout,
the expression of miRNAs in rainbow trout eggs and
early embryos have been studied and some novel
eggpredominant miRNAs were identified [44,45]. In this
study, post-ovulatory aged rainbow trout eggs with
different qualities were collected and used to generate
miRNA transcriptome profiles for identifying specific
miRNAs associated with egg quality, which could
potentially be used as biomarkers for evaluating egg quality.
The study also provides new information and insights
for future studies to elucidate the gene regulatory
networks involved in the control of egg quality.
Identification of known and novel miRNAs in eggs of
Small RNA libraries constructed from eggs of different
ages post-ovulation were subjected to deep sequencing
using the Illumina platform. The massive sequencing
produced 27,342,477, 26,910,438 and 29,185,371 reads
from the libraries constructed from D1PO, D7PO and
D14PO eggs, respectively (Table 1). After removing the
impurity sequences, known mRNAs, non-coding RNA
families without miRNA, and repetitive DNA sequences,
the remaining 23,554,621, 26,144,764 and 28,408,709
sequences from these three samples were used for
identification of known miRNAs and prediction of new miRNAs.
A total of 2,945,228, 2,261,910 and 1,464,754 sequences
from D1PO, D7PO and D14PO eggs, respectively, were
mapped to miRbase database (Release 21). Based on the
criteria described in Methods section for known
miRNAs, a total of 496 known miRNAs were identified from
the three samples (Additional file 1: Table S1). Predication
of novel miRNAs was carried out according to the criteria
that the extended sequences of the miRNAs at the aligned
rainbow trout genomic locations have the propensity of
forming hairpin structures, and the sequences do not
meet the criteria of known miRNAs. The number of novel
miRNAs predicted from our datasets was 306 (Additional
file 1: Table S2).
Identification of differentially expressed miRNAs in eggs
of different qualities
A three-way comparison of the miRNAs among the
samples indicated that D1PO, D7PO and D14PO eggs shared
392 known and 236 novel miRNAs (Figure 1). A total of
414, 481, and 470 known miRNAs and 243, 298, and 296
novel miRNAs were identified from D1PO, D7PO, and
D14PO samples, respectively (Table 2). To identify miRNAs
Table 1 Number of sequences generated from small RNA
libraries of eggs with different qualities
Reads mapped to miRbase
27,342,477 26,910,438 29,185,371
23,554,621 26,144,764 28,408,709
Reads for novel miRNA prediction 13,421,585 14,105,022 15,709,247
Figure 1 Three way Venn diagrams showing the number of
miRNAs among D1PO, D7PO and D14PO eggs. (A). Known
miRNAs identified. (B). Novel miRNAs predicted. D1PO, D7PO, and
D14PO are day 1, 7, and 14 post-ovulatory eggs, respectively.
Table 2 Number of known and novel miRNAs identified
from eggs of different qualities
that are related to egg quality, the miRNA reads in D1PO
and D14PO samples were quantile normalized and
compared. A total of 189 miRNAs showed differential
expression between the 2 samples (fold change greater
than 3). Eighty-eight miRNAs showed higher expression
in high quality eggs, while 101 miRNAs displayed higher
expression in low quality eggs (Additional file 1: Table S3).
Differentially expressed miRNAs with a fold change greater
than 10 are shown in Figure 2. Interestingly, the majority
of the miRNAs highly expressed in D1PO eggs are
known miRNAs (70.45%), while majority of the miRNAs
with higher expression in D14PO eggs are novel
miRNAs (64.36%) (Figure 3).
The differentially expressed miRNAs with a fold change
greater than 10 and normalized reads greater than 50 in
both samples were subjected to real-time quantitative PCR
(RT-qPCR) analysis. Based on melting curve analysis, we
selected 7 miRNAs that showed specific amplifications for
further analysis (Additional file 2: Figure S1). Six of the 7
miRNAs that were successfully analyzed by RT-qPCR
showed significantly higher expression in D1PO vs. D14PO
eggs, which is consistent with the deep sequencing results,
although the magnitude of fold changes shown by the
two methods was not the same (Figure 4). Of the 6
miRNAs, 4 are known miRNAs (omy-miR-193b-3p,
omymiR-203c-3p, omy-miR-499-5p and omy-miR-7550-3p)
and 2 are novel miRNAs (omy-miR-nov-95-5p,
Analysis of predicted targets of the differentially
PITA and miRanda algorithms were used to predict the
target genes of the 6 differentially expressed miRNAs
that were validated by RT-qPCR analysis, A total of 178
gene entries from gene index database (http://www.
animalgenome.org/repository) were predicted, which
represent 114 known genes and 23 unknown genes
(Additional file 1: Table S3). In addition, when
mitochondrial genome was used as a query, a gene encoding
cytochrome c oxidase subunit 1 (COX6B1) was predicted as
the target of omy-miR-nov-95-5p. GO functional
enrichment analysis of the target genes was carried out using
Blast2GO software . The results indicated that the top
three GO terms (second level) in biological process include
cellular process, metabolic process, and single organismal
process, and the most significant GO terms in molecular
function are binding, catalytic activity, and transporter
activity (Figure 5). In comparison with the recent
transcriptome data in rainbow trout , the significantly enriched
GO terms are single-organism process and membrane.
Interestingly, the GO term of cell death, which is one of
the indicators of egg quality, is under the children branches
of single-organism process.
Figure 2 Expression profiles of miRNAs in eggs of different qualities. The miRNAs with more than 10 times difference in expression
between D1PO and D14PO eggs are shown.
GO enrichment analysis using 96,546 rainbow trout
unique transcript sequences as the background showed
that 23, 23, and 90 GO terms in cellular component,
molecular function, and biological process, respectively, are
significantly enriched (Adjusted p-value < 0.05) (Additional
file 1: Table S5). The genes associated with the significantly
enriched GO terms are mainly involved in response to
stress and DNA/RNA damage (RPB4, RECQ4A, CHD1L,
WDR61, and CNOT1), cell death and signal
transduction (RASSF5, GEM, RAB14, and CACNA1E), energy
Figure 3 Percentage of known and novel miRNAs showing
higher expression in high quality eggs (D1PO) or low quality
and transcription regulation (ATP5A1, COX6B1, CDT1,
GTF2A2, SIX1, and GMEB2).
Using deep sequencing in combination with RT-qPCR,
we have identified 6 miRNAs that are associated with
egg quality in rainbow trout. These miRNAs could
potentially be used as biomarkers for prediction of egg quality
in rainbow trout. Our results showed that the numbers of
known and novel miRNAs do not show dramatic changes
among eggs of different qualities (Table 2), however, most
of the highly expressed miRNAs in high quality eggs are
known miRNAs and most of the highly expressed
miRNAs in low quality eggs are novel miRNAs.
In the study, we used post ovulatory aged eggs with
different fertilization rates to identify miRNAs that are
associated with egg quality. The D1PO, D7PO and D14PO
eggs contained both good and bad eggs of varying
proportions resulting from temporal change of eggs held in
the body cavity. Therefore, the miRNAs among these egg
samples have only quantitative discrepancy. It is
conceivable that identification of evident miRNA expression
discrepancy among these samples with varying proportion of
good and bad eggs is challenging, and such difficulties
have also been documented in previous studies [25,48].
The SYBR green based RT-qPCR method for miRNA
Figure 4 RT-qPCR validation of differentially expressed miRNAs identified by deep sequencing between D1PO and D14PO eggs. Data
were normalized using -actin and 18S rRNA. The means of the normalized miRNA expression values (n = 4 pools) were calculated and expressed
as relative fold changes. Only omy-miR-192a-5p does not match the sequencing data.
detection has its limitations. As both the universal primer
and the miRNA specific primer are fixed, it makes
optimization of the assay very difficult. Only 7 miRNAs
showed specific amplification based on melting curve
analysis and many more did not show specific amplifications,
although different annealing temperatures were tried.
Mitochondrion is not only vital in ATP generation and
maintenance of cell homeostasis, but also central in the
apoptotic signaling pathways . It has been reported
that some miRNAs were involved in the regulation of
mitochondrion-mediated apoptosis [50,51]. In this study,
one of the differentially expressed novel miRNAs,
omymiR-nov-95-5p, was predicted to target mitochondrial
gene (COX6B1). COX6B1 is known to catalyze the
electron transfer from reduced cytochrome c to oxygen in
respiratory chain, and deficiency of cytochrome c oxidase is
linked to many human diseases . Therefore,
downregulated expression of omy-miR-nov-95-5p in aged eggs
may cause abnormal expression of COX6B1, thereby
affecting normal mitochondrial respiratory chain and egg
quality. In addition, many predicted target genes of
omymiR-nov-95-5p and omy-miR-193b-5p are associated with
significantly enriched GO terms, which include cell death,
stress response, DNA damage and repair, and RNA
degradation. These genes include RASSF5, RPB4, RECQ4A,
CHD1L, WDR61, and CNOT1. Abnormal expression of
these important genes may also contribute to decreased
quality of D14PO eggs. Some of the differentially expressed
miRNAs identified in this study, such as miR-449 and
miR-203, have been reported to affect cell death and tumor
suppression [53,54], but the other differentially expressed
miRNAs have not been characterized with regard to
their functions. Therefore, a comprehensive study of
these miRNAs and their target genes would help
understanding the factors contributing to egg quality.
Many factors can affect miRNA expression [41,55]. In
rainbow trout, the eggs kept in the cavity for extended
time are associated with reduced levels of IGF I and IGF
II, and increased levels of KRT8, CTSZ and other
transcripts [25,48]. Increased activities of GOT1, ACPP, LVII
fragments and others biomolecules in the coelomic fluid
have also been shown to be related to egg quality [22,26].
In addition, the levels of 17 20-P and 17-OH-P in
blood vary significantly before and after ovulation . It
is not known if these changes may directly or indirectly
affect miRNA expression, leading to the changes in target
gene expression. Furthermore, some miRNAs have
regulatory roles in controlling other miRNAs . Therefore, in
order to understand the mechanisms underlying the
changes in egg quality such as those associated with
post-ovulation aging in rainbow trout, it would be
important to systematically study the interaction networks
among physiological and environmental factors
affecting egg quality, the miRNAs and their target genes.
This study identified 6 differentially expressed miRNAs
that are associated with egg quality in rainbow trout.
Further characterization of these miRNAs, especially
the novel ones, and their target genes may provide
valuable information on the roles of these miRNAs in
controlling egg quality, and ultimately lead to the
development of novel biomarkers for evaluation of egg
quality in rainbow trout.
Figure 5 Top 3 GO terms (second level) of the target genes of 6
miRNAs highly expressed in high quality eggs (D1PO) compared
with the same GO terms of whole transcriptome in rainbow
trout. (A) Molecular function. (B) Biological process. (C) Cellular
component. Solid fill bar: Top 3 GO terms of the target genes of 6
differentially expressed miRNAs; Pattern fill bar: the corresponding
GO terms analyzed using whole genome transcriptome. ** indicates
significant difference at P < 0.001.
Sample collection and determination of fertilization rate
Samples from commercial populations of Troutlodge Inc.
(Sumner, WA) were reared for at least two generations
using commercial trout feed under the temperature of
12.0-12.5C in treated spring water recirculating partially
in the National Center for Cool and Cold Water
Aquaculture (Kearneysville, WV). The fish were checked daily for
ovulation as described previously . Eggs were collected
from 32 females on day 1, day 7, and day 14
postovulation (D1PO, D7PO, and D14PO) as described
previously [57,58]. The eggs for RNA isolation were placed
in microcentrifuge tubes and frozen in liquid nitrogen
after removal of coelomic fluid, and then stored in 80C
freezer until extraction of RNA.
Eggs fertilized using pooled milt from at least three
males were placed in Davidsons solution for microscopic
analysis to determine fertilization rate. Fertilization rate
was based on cleavage evaluation of 50 embryos per group
as described previously [57,58]. The fertilization rates for
D1PO, D7PO, and D14PO were 91.8%, 73.4% and less
than 50%, respectively.
Sequencing and analysis of egg miRNAs
Total RNA from the eggs was isolated using Trizol
reagent (Invitrogen, Carlsbad, CA) according to the
manufacturers instructions followed by additional purification
steps with lithium chloride precipitation. The RNA
integrity was evaluated by gel electrophoresis, and the
RNA purity was checked by the ratio of OD260/OD280.
The RNAs isolated from eggs of different females were
pooled, and the pooled RNAs were used for sequencing
of miRNAs, which was performed on an Illumina GAIIx
by LC Sciences (Huston, TX) as described previously
. The software package, ACGT101-miR v3.5 from
LC Sciences (Houston, TX), was used for analyzing the
sequencing data. Sequences with low resolution, copy
number less than 10, length shorter than 15 nt or longer
than 26 nt, adapter sequences, junk sequences, and simple
sequence were filtered out. In addition, the sequences
mapped to the databases of mRNA (ftp://ftp.ncbi.nih.gov/
genomes / D _ rerio/R NA / Gnomon _ mRNA.fsa.gz), Rfam
(http://rfam.janelia.org) and Repbase (http://www.girinst.
org/repbase) were also removed. The remaining sequences
were used to BLAST against all miRNAs in the miRBase
database (release 21) to identify known miRNAs (less than
2 mismatches in the first 18 nt or E-values equal or
smaller than 0.01) [59,60]. The sequences that did not
match known miRNAs were mapped to the rainbow trout
genome  to identify potentially novel miRNAs. Novel
miRNAs were predicted if the extended sequences (~60 nt
in both directions) at the aligned positions have the
propensity to form hairpin structures as analyzed using
The reads for each miRNA (either known or novel)
were aligned by Clustal analysis using CLC Genome
Workbench (CLC bio, MA). Quantile normalization in
Limma package was used to normalize the miRNA reads
. The difference in miRNA expression between high
quality egg (D1PO) and low quality egg (D14PO) was
evaluated by Z-test .
RT-qPCR analysis of miRNA expression
Two g of DNase-treated RNAs (4 pools from D1PO or
D7PO or D14PO eggs) were converted to cDNA using
miScript reverse transcriptase mix (Qiagen, Valencia, CA).
The cDNA samples were used for RT-qPCR quantification
and melting curve analysis of miRNAs using miRNA
specific primers (Additional file 1: Table S6) in combination
with the miScript universal primer (Qiagen, Valencia, CA).
Rainbow trout -actin and 18S rRNA genes were used as
endogenous controls. RT-qPCR was performed in
duplicate for each cDNA on a Bio-Rad CFX96 system. The iQ
SYBR Green Supermix (Bio-Rad, Hercules, CA) was used
in 20 l reaction volumes containing 100 nM of each
primer and 5 l of 1:150 diluted cDNA. Cycling parameters
were 95C for 3 min followed by 40 cycles of 95C for
10 sec and 50 to 62C for 1 min. Melting curve analyses
were programmed following the amplifications. Standard
curves for all miRNAs and the endogenous controls were
constructed using a serial dilution of a pooled cDNA
sample. For each sample, the quantity of the specific miRNAs
and the reference genes was determined from respective
standard curves. The mean quantity of the specific
miRNAs was then divided by the geometric mean of the 2
reference genes to obtain a normalized value. Mean
differences in expression levels were reported as relative
fold changes using the lower expression value as a
calibrator. Differences in miRNA expression were
determined by one-way analysis of variance (ANOVA) using
Identification of miRNA targets via computational analysis
Two miRNA target prediction algorithms, miRanda
(http://www.microrna.org/microrna/home.do)  and
exe.html)  were used to identify the target genes of
the egg quality related miRNAs. Sequences of 15,387
3UTRs and 14,788 coding regions fetched from
rainbow trout transcripts as described previously  were
used in the analysis. The rainbow trout mitochondrial
genome sequence (L29771) was downloaded from the
GenBank database . The thresholds of miRanda for
candidate target sites were paring score S 150 and
energy score G 18 kcal/mol, where S is the sum of
single-residue-pair match scores over the alignment trace
and G is the free energy of duplex formation from a
completely dissociated state which was calculated using
the Vienna package . The score G 15.0 was used
for PITA .
Gene ontology analysis
The online software blast2go (http://www.blast2go.com)
was used to analyze the target genes of differentially
expressed miRNAs. Enrichment of the gene ontology (GO)
terms was tested using hypergeometric function as
described earlier , and the 96,546 rainbow trout
unique transcript sequences (http://www.animalgenome.
org/repository/aquaculture) were used as background.
Adjusted p-values by Benjamini and Hochberg false
discovery rate (FDR) method were used to determine
significance of GO enrichment.
Additional file 1: Table S1. Known miRNAs identified from rainbow
trout eggs of different quality. Table S2. Novel miRNAs predicted from
rainbow trout eggs of different quality. Table S3. Differentially expressed
miRNAs in D1PO vs. D14PO eggs (fold change greater than 3). Table S4.
The predicted target genes of 6 differentially expressed miRNAs between
D1PO and D14PO eggs validated by RT-qPCR analysis. Table S5. Target
genes associated with siginificamtly enriched GO terms. Table S6 Primers
used to validate differentially expressed miRNAs.
Additional file 2: Figure S1. Melt peak charts of 7 miRNAs showing
specific amplifications in RT-qPCR analysis. -actin and 18S rRNA are
endogenous control genes.
HM designed the experiment, carried out RNA isolation, sequencing data
collection, and RT-qPCR analysis, performed bioinformatical and statistical
data analysis, and drafted the manuscript. GW conceived the study, tested
fertilization rates, and collected samples. MH tested fertilization rate and
collected samples. HW participated in data analysis and provided resources
for data analysis. LW helped in data analysis. JY contributed to overall
project design and manuscript preparation. All authors read and approved
the final manuscript.
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