Genome-wide identification and comparison of differentially expressed profiles of miRNAs and lncRNAs with associated ceRNA networks in the gonads of Chinese soft-shelled turtle, Pelodiscus sinensis

doi:10.21203/rs.2.10525/v1

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Research article

Genome-wide identification and comparison of differentially expressed profiles of miRNAs and lncRNAs with associated ceRNA networks in the gonads of Chinese soft-shelled turtle, Pelodiscus sinensis

https://doi.org/10.21203/rs.2.10525/v1

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published 29 Jun, 2020

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Abstract Background: Gonad is the major factor affecting the animal reproduction. The regulation mechanism of protein coding genes expression involved reproduction is still remains to be elucidated. Increasing evidence has shown that ncRNAs play key regulatory roles in gene expression in many life processes. The roles of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in reproduction had been investigated in some species. However, the regulation patterns of miRNA and lncRNA in sex biased expression of protein coding genes remains to be elucidated. In this study, we performed an integrated analysis of miRNA, messenger RNA (mRNA), and lncRNA expression profiles to explore their regulatory patterns in the female ovary and male testis of the soft-shelled turtle, Pelodiscus sinensis. Results: We identified 10 796 mature miRNAs, 44 678 mRNAs, and 58 923 lncRNAs in the testis and ovary. A total of 16 817 target genes were identified for miRNAs. Of these, 11 319 mRNAs, 10 495 lncRNAs, and 633 miRNAs were expressed differently. The predicted target genes of these differential expression (DE) miRNAs and lncRNAs included genes related to reproduction regulation. Furthermore, we found that 5 408 DElncRNAs and 186 DE miRNAs showed sex-specific expression. Of these, 3 miRNAs and 917 lncRNAs were testis specific and 186 DEmiRNAs and 4 491 DElncRNAs were ovary specific. We constructed compete endogenous lncRNA-miRNA-mRNA networks using bioinformatics, including 273 DEmRNAs, 5 730 DEmiRNAs, and 2 945 DElncRNAs. The target genes for the different expressed of miRNAs and lncRNAs included Wt1, Creb3l2, Gata4, Wnt2, Nr5a1, Hsd17, Igf2r, H2afz, Lin52, Trim71, Zar1, and Jazf1, etc. Conclusions: In animals, miRNA and lncRNA regulate the reproduction process, including the regulation of oocyte maturation and spermatogenesis. Considering their importance, the identified miRNAs, lncRNAs, and their targets in P. sinensis might be useful for genome editing to produce higher quality aquaculture animals. A thorough understanding of ncRNA-based cellular regulatory networks will aid in the improvement of P. sinensis reproduction traits for aquaculture.

Epigenetics & Genomics

gonad

miRNA

lncRNA

ceRNA

Sexual reproduction is a critical process for all animals. Hormones and genes shape the morphological, behavioural, and reproductive abilities of both sexes throughout their lives. Sex-dependent differences are significantly exhibited in the growth and size of aquaculture animals. Reproduction is an important yet complex biological process in animals, and a comprehensive understanding of the genetic mechanisms underlying the reproductive traits, particularly from the genomics perspective, is important. The Chinese soft-shelled turtle (Pelodiscus sinensis) is an important freshwater aquaculture species in China. The turtle has a sex-dependent growth pattern, with males showing a significantly larger weight and size, thicker and wider calipash, and lower levels of fat than those of the females [ 1]. Similar to other reptiles and mammals, the soft-shelled turtle has the ability to store sperm in the ovary [ 2]. Spermatogenesis, copulation, and ovulation are seasonal and segregational in turtles [ 2, 3]. Many previous studies have focused on sex determination and differentiation in the turtle. However, to the best of our knowledge, no study has explored the genetic mechanisms underlying reproductive development of the soft-shelled turtle.

The genomes of different species from worm to human show similar numbers of protein-coding genes [ 4], prompting the notion that many aspects of complex organisms arise from non-protein-coding regions. The transcriptome profiling of non-protein-coding RNAs by next-generation sequencing has been successfully used to investigate the transcripts and their expression levels. Non-coding RNAs (ncRNAs) regulate gene expression at transcriptional and post-transcriptional levels. Increasing evidence has highlighted that ncRNAs involved in reproduction process [

                 5
              </a>, <a href="#_ENREF_6">
              
                 6
              </a>]. </p>

Regulatory ncRNAs could be divided in three categories based on transcript size: small (sncRNAs), medium, and long (lncRNAs) [ 7]. MicroRNAs (miRNAs) are an abundant class of sncRNAs (~22 nt long), which negatively regulate gene expression at the messenger RNA (mRNA) level [ 8]. lncRNAs are a large and diverse class of transcribed non-protein-coding RNA molecules, more than 200 nucleotides in length[ 8]. miRNAs regulate gene expression at post-transcriptional levels by binding to either perfect or imperfect complementary in the 3′ untranslated regions (UTRs) of targets, and trigger either the degradation of the targets or inhibit their translation [ 9]. It is known that lncRNAs influence up-regulatory and down-regulatory expression at the transcriptional and post-transcriptional levels. lncRNAs regulate gene expression by epigenetic modification, transcription, and post-transcription via DNA methylation, histone modification, and chromatin remodelling [ 10]. lncRNAs can also bind the typical classes of transcription factor binding sites enriched in promoters, which regulate gene expression [ 11].

In non-mammal vertebrate animals, large scale identification of miRNAs and lncRNAs has been implemented in many species. miRNAs have been shown to engage in the regulation of gene expression that play key roles in follicular development, granulose cell function, oocyte maturation, and ovary pathophysiology [ 12, 13]. In non-mammal animals, miRNAs also play important roles in ovary development [ 14]. A previous study showed that miR-30 was responsible for maternal mRNA clearance during the embryonic development of zebrafish [ 15]. MiR-9 could bind to the foxl3 3′ UTR of Monopterus albus, which may be involved in the process of oocyte degeneration [ 16]. In mature Paralichthys olivaceus gonads, miR-143 and miR-26a showed sex bias expression [ 17]. miRNA is also critically involved in spermatogenesis in mammals [ 18, 19].

Studies have provided evidence that lncRNAs regulate the processes of mammal reproduction, including germ cell specification, sex determination, gonadesis, gametogenesis, placentation, and pathologies affecting reproductive tissues [ 20-22]. The knockouts of lncRNAs can cause a partial or complete loss of male fertility in Drosophila [ 23]. In mice, mrhl RNA can negatively regulate Wnt signalling and becomes down-regulated upon the meiotic progression of spermatogonial cells [ 24, 25]. In female mammals, lncRNA also plays an important role in fertility. H9 knockout female mice showed altered folliculogenesis and increased follicular atresia, which might be due to the lack of H9 decreasing the expression of Amh by binding the 3′ UTR of Amh mRNA [ 26]. In Daphnia magna, lncRNA Dapalr could transactivate and maintain dsx1 expression, which produces males in response to environmental stimuli [ 21].

A large number of ncRNAs has been discovered due to the advances in genomics and molecular biology. However, the regulation of the reproduction system is complex. Recently, the mechanism of competing endogenous RNAs (ceRNA) was reported as a specific regulatory pathway of lncRNA, miRNA, and mRNA to explain how they exert their influence on protein levels [ 27-29]. LncRNAs, as competing endogenous ceRNAs, can indirectly regulate mRNAs by acting as miRNA sponges. Investigations regarding the lncRNA–miRNA–mRNA networks provide a better understanding of the role of the lncRNA–miRNA interaction in mRNA regulation. This might provide new insights for understanding the endogenous differential expression of mRNA in both sexes.

Although miRNAs and lncRNAs have shown that they regulate mammalian tissue development and reproduction, little is known about their sexual dimorphism in gonads and reproduction in turtle families and other reptiles. In the present study, miRNAs and lncRNAs of the ovary and testis were investigated in P. sinensis to explore novel ncRNAs in sexual dimorphism and reproduction. Results of the present study may provide the basis for a better understanding of the roles of miRNAs and lncRNAs in the turtle ovary and testis, leading to exploitation of the mechanisms of reproduction in Chinese soft-shelled turtle.

Overview of the sequencing data

We constructed cDNA libraries of miRNAs, mRNAs, and lncRNAs using the RNA from the ovary and testis. After filtering out low-quality transcripts, 5′ and 3′ adapters, and reads <18 nt, a total of 113.5 M clean reads were produced by Illumina technology for miRNAs. The 21 and 22 nt length tanscripts were the most abundant (Fig. 1a), and 60.4% high-quality tanscripts were mapped to the turtle genome (Pelsin-1.0, NCBI). We obtained 153.25 Gb clean reads for mRNA and lncRNA sequencing and approximately 84.41% to 87.72% clean reads were mapped to the turtle genome after removing low-quality reads (Fig. 1c). After mapping the genome, approximately 84.41% ~ 87.72% of the reads were mapped to inergenic regions in the P. sinensis reference genome (Fig. 1d, Additional 1).

Identification of differentional expression mRNAs, miRNAs, and lncRNAs

According to the miRNA expression profiles, we detected 10 796 novel miRNAs, and the miRNAs were mapped to 16 817 genes. A total of 633 miRNAs were significantly differently expressed between the ovaries and testes (P < 0.05), including 138 up-regulated miRNAs and 495 down-regulated miRNAs (Fig. 2a, Additional file 2). These miRNAs belong to 438 families (Additional file 3). A total of 44 678 protein coding genes were detected after gene alternative splicing analysis and optimising gene structure, including 27 343 novel genes, of which 10 596 genes were mined by function annotations. Furthermore, the mRNA reads were mapped to 17 334 annotated genes in the genome, and 6304 mRNAs were differently expressed based on sex (Fig 2b). A total of 58 923 lncRNAs were detected and 10 495 lncRNAs were significantly differently expressed between ovaries and testes (Fig 2c). Among the sex-biased lncRNAs and miRNAs, 3 miRNAs and 917 lncRNAs exhibited testis-specific expression, and 186 miRNAs and 4491 lncRNAs showed ovary-specific expression. Among these DEmiRNAs, we identified a set of genes regulating animal reproduction, including miR-133, miR-138, miR-145, miR-143, miR-378, etc. Furthermore, these miRNAs belonged to 438 families (Additional file 4).

Prediction of the potential targets of lncRNAs in cis and trans was performed to investigate the function of lncRNAs (Additional file 5). After searching for protein-coding genes 100 kb upstream and downstream, 3 904 DElncRNAs were found to correspond to regulating protein-coding genes in cis. The results included Foxl2, Cyp19a1, Gper, Esr, Dazl, and Sox30, etc, which suggest that the reproductive process might be regulated by the action of these lncRNAs on the protein-coding genes. Conversely, we identified 2 160 lncRNAs showing trans action by LncTar, including a set of genes that might regulate reproduction.

Functional analysis of DEmiRNAs and DElncRNAs

To annotate the molecular function of the differentially expressed miRNAs, both the RNA hybrid and miRanda software were used to improve the prediction of miRNA targets, resulting in 16 817 targets potentially regulated by 6560 miRNAs. GO categories of miRNAs and lncRNAs were assigned to all target genes based on the following three ontologies: cellular component, molecular function, and biological process (Fig. 3a, Additional file 6, 7, 8). Functions of target genes in the cellular component category were mainly focused on cell part, cell, and membrane. Based on the molecular function, the most abundant targets genes were focused on binding, followed by catalytic activity. Regarding the biological process, the most abundant target genes were focused on the single organism process, followed by cellular process, and biological regulation.

KEGG pathway enrichment analysis revealed that the DEmiRNAs were involved in 167 signalling pathways (Additional file 9). The identified metabolism networks were related to neuroactive ligand-receptor interaction and the regulation of the actin cytoskeleton. The most abundant target genes of DEmiRNAs focused on glyoxylate and dicarboxylate metabolism. We detected at least 12 pathways involved in reproductive biology, including oocyte meiosis, TGF-β signalling, ovarian steroidogenesis, GnRH signalling, Wnt signalling, cAMP signalling, steroid biosynthesis, MAPK signalling, p53 signalling, RNA polymerase, metabolism of xenobiotics by cytochrome P450, and mTOR signalling.

KEGG pathway enrichment analysis showed that the DElncRNAs were involved in 221 signalling pathways in a cis-regulatory manner and 225 signalling pathways in a trans-regulatory manner (Additional 10, 11). The KEGG pathway enrichment analysis revealed that the DGE lncRNAs were involved in oocyte meiosis, steroid hormone biosynthesis, Wnt signalling pathway, GnRH signalling pathway, p53 signalling pathway, apoptosis, MAPK signalling pathway, AMPK signalling pathway, TGFβ signalling pathway, cAMP signalling pathway, RIG-I-like receptor signalling pathway, mTOR signalling pathway, and insulin signalling pathway.

Validation of differentially expressed miRNAs and lncRNAs

To validate the sequencing data of miRNAs and lncRNAs, qRT-PCR was employed to test the relative expression of eight differentially expressed miRNAs (novel-miR-1236, novel-miR-1361, novel-miR-2322, novel-miR-6721, novel-miR-10042, novel-miR-10231, novel-miR-10322, and novel-miR-10468) and seven differentially expressed lncRNAs (MSTRG.435295.1, MSTRG.129036.2, MSTRG.88998.1, MSTRG.281180.2, MSTRG.127189.1, MSTRG.561412.1, and MSTRG.100955.1) in ovaries and testes. Among the miRNAs, novel-miR-1361, novel-miR-2322, novel-miR-6721, novel-miR-10042, novel-miR-10231, novel-miR-10322, and novel-miR-10468 were downregulated in testis, while novel-miR-1236 were upregulated in testes (Fig. 3a). Among the lncRNAs, MSTRG.435295.1, MSTRG.88998.1, MSTRG.127189.1, MSTRG.100955.1 were upregulated in testes, while MSTRG.129036.2, MSTRG.281180.2, MSTRG.561412.1 were downregulated in testis (Fig. 3b). The expression of these miRNAs and lncRNAs patterns among different groups were well-matched with the RNA-Seq data, which could guarante the accuracy of subsequent functional analysis.

Construction of ceRNA network

We constructed ten ceRNA networks of the mRNAs, miRNAs, and lncRNAs related with the DGE by integrating the expression profiles and regulated relationships among the mRNA, lncRNAs, and miRNAs from the high-throughput sequencing data (Additional file 12). The database included many genes that regulated spermatogenesis and oocyte maturation, such as Cyp19a1, Gper, Esr1/2, Sox30, Dazl, and Foxl2. The relationships among DElncRNAs, DEmiRNAs, and DEmRNAs were evaluated. We screened miRNAs that included miRNA response elements, which could bind with both lncRNAs and mRNAs. The DEmiRNAs included novel-miR-227, novel-miR-9914, novel-miR-6375, novel-miR-1222, novel-miR-6721, novel-miR-2026, novel-miR-6671, novel-miR-642, novel-miR-6319, and novel-miR-42, etc. The ceRNA networks included a set of mRNAs regulating reproduction (Fig. 4a, 4b, Additonal file 12). For instance, Dazl mRNA and MSTRG.71049.8 shared a common binding site of miRNA novel-miR-1222. We also identified Wt1, CREB3l2, Gata4, Wnt2, Nr5a1, Hsd17, Igf2r, H2afz, Lin52, Trim71, Zar1, and Jazf1 in the ceRNA network. These miRNAs and mRNAs participate in regulating the reproductive process, including meiosis and spermatogenesis.

The turtle genome showed a large portion of non-coding regions, indicating that this part of the genome carried more information than that of the protein-coding genes. Increasing evidence has shown that miRNA and lncRNA emerge as regulators in animal reproduction via controlling gene expression[ 26]. However, their exact functions in the soft-shelled turtle remain poorly understood. Despite limited studies that have identified lncRNAs in the turtle [ 1], the miRNAs and lncRNAs in the database are still paucity. In the present study, to understand the molecular mechanism involved in the reproduction of P. sinensis, we analyzed a genome-wide expression of miRNAs, lncRNAs, and mRNAs in the mature ovaries and testes during the reproduction season. After filtering, we obtained 10 796 miRNAs and 58 923 lncRNAs, with these miRNAs and lncRANs not reported previously in the miRBase and lncRNA database. The length of miRNAs ranged from 18 to 25 nt. In a previous study, Huang et al. [

                 30
              </a>] identified only 10 miRNAs in <em>P</em>.<em> sinensis</em> based on ESTs and GSSs information using the bioinformatics approach. Zhang et al.[<a href="#_ENREF_1">
              1</a>] identified 5994 lncRNAs by high-throughput sequencing in the juvenile turtle gonads.
           MiRNAs and lncRNAs have been shown to have a stage-manner expression in animals[<a href="#_ENREF_14">
              
                 14
              </a>, <a href="#_ENREF_31">
              
                 31
              </a>]. The different developmental stages and the methods utilised in the different studies
           might be responsible for the discrepancies found.</p>

We obtained 633 DE miRNAs and 10 495 DE lncRNAs. A total of 8 miRNAs and 7 lncRNAs were verified using qRT-PCR. Among these miRNAs, 7 miRNAs were upregulated in testis, while 1 miRNA was downregulated. For the lncRNAs, 4 lncRNAs was up regulated, while 3 lncRNAs were downregulated. The qRT-PCR results were well matched to the high-throughput sequencing data.

The mature miRNAs and lncRNAs is crucial in the regulation of gene expression in different way [

                 32
              </a>, <a href="#_ENREF_33">
              
                 33
              </a>]. GO annotations for the targets were obtained using topGO software. The most abundant
           differentially expressed gene were involved in the single organism process, followed
           by the cellular process and biological regulation. More than 1000 DEmiRNAs were involved
           in the reproductive process and reproduction. The GO analysis of DEmiRNA and DElncRNA
           showed that some terms under the biological process and molecular function categories
           were related to sex-specific reproduction.</p>

The miRNAs identified in the soft-shelled turtle belonged to 439 families after mapping on the genome, including miR-130, miR-133, miR-138, miR-145, miR-143, miR-202, miR-224, and miR-378. In the majority of cases, the predicted targets in the present study were consistented with the published studies in other animals. MiR-202-3p could regulate the human sertoli cells proliferation, apoptosis, and synthesis functionby targeting LRP6 and cyclin D1 which belong to Wnt/β-catenin signalling pathway [

                 34
              </a>]. Sun et al. [<a href="#_ENREF_35">
              
                 35
              </a>] reported that miR-378 could indirectly regulate oocyte maturation, which is possibly
           via inhibiting oocyte-cumulus apoptosis in mice. A similar function of miR-378 in
           porcine has been observed [<a href="#_ENREF_36">
              
                 36
              </a>]. SMAD5 and MSK1 were the miR-130b targets. In cumulus cells, miR-130b could altered the lactate
           production and cholesterol biosynthesis, and miR-130b could inhibit oocyte maturation
           <em>in vitro</em> via reducing the first polar body extrusion, the proportion of oocytes reaching the
           metaphase II stage, and mitochondrial activity [<a href="#_ENREF_37">
              
                 37
              </a>]. </p>

MiRNAs are not only involved in the ovary but are also involved in testis development and reproduction. MiRNA-20 and miRNA-106a promote the renewal of spermatogonial stem cells via targeting Stat3 and Ccnd1[

                 38
              </a>]. In mice, miR-224 promotes spermatogonial stem cell differentiation and self-renewal
           via targeting <em>Dmrt1 </em>[<a href="#_ENREF_39">
              
                 39
              </a>]. Overexpression of miR-224 increased the expression of GFRα1 and PLZF by the down-regulation
           of <em>Dmrt1</em>. Beyond that, miR-130a [<a href="#_ENREF_40">
              
                 40
              </a>], miR-200c [<a href="#_ENREF_41">
              
                 41
              </a>], let-7a-3p [<a href="#_ENREF_42">
              
                 42
              </a>], and miR-106 [<a href="#_ENREF_38">
              
                 38
              </a>] also play an important role in animal reproduction.</p>

LncRNAs are recognised as an important functional regulatory factors in eukaryotic gene regulation, and play an important role in the regulation of gene expression in a variety of biological processes in multiple species. The function of lncRNAs occurs across a range of animal reproduction processes including sex determination, meiosis, spermatogenesis, and imprinting via epigenetic processes including DNA and histone methylation, chromatin looping, and nucleosome positioning [

                 33
              </a>, <a href="#_ENREF_43">
              
                 43
              </a>]. In <em>Drosophila</em>, knocked out testis-specific lncRNAs exhibited a partial or complete loss of male
           fertility [<a href="#_ENREF_23">
              23</a>]. LncRNA <em>H19</em> could regulate the IGF-1 signalling pathway and result in regulating the proliferation
           and apoptosis of male germline stem cells in bovines [<a href="#_ENREF_44">
              
                 44
              </a>]. Furthermore, the <em>H19</em> imprinting control region could acquire parent-of-origin-dependent methylation after
           fertilisation independently of the chromosomal integration site or the prerequisite
           methylation acquisition in male germ cells [<a href="#_ENREF_45">
              
                 45
              </a>]. LncRNA <em>THOR</em> contributes to the mRNA stabilisation activities of <em>IGF2BP1</em> and are isolated to spermatocytes during meiosis II, and the knockout of <em>THOR</em> resulted in fertilisation defects in zebrafish [<a href="#_ENREF_46">
              
                 46
              </a>]. However, most lncRNAs evolve rapidly and are less conserved with more than 80%
           of lncRNA families being of primate origin [<a href="#_ENREF_47">
              
                 47
              </a>], and further investigation should be undertaken to reveal the function in the turtle.</p>

MicroRNAs are negative regulators of gene expression, decreasing the stability of target RNAs or limiting their translation. Recently, evidence has shown that lncRNA and mRNA could bind the miRNA binding site, and that miRNA acts as a sponge [

                 27
              </a>, <a href="#_ENREF_48">
              
                 48
              </a>]. In the present study, we constructed lncRNA–miRNA–mRNA networks for sex different
           expression based on the high-throughput sequencing data in the turtle. We characterised
           DEmiRNAs and DElncRNAs by the target mRNA, including <em>Wt1</em>, <em>CREB</em>, <em>Gata4</em>, <em>Wnt2</em>, <em>Nr5a1</em>, <em>Hsd17</em>, <em>Igf2r</em>, <em>H2afz</em>, <em>Lin52</em>, <em>Trim71</em>, <em>Zar1</em>, and <em>Jazf1</em>. The target genes of miRNAs and lncRNAs play important roles in the reproduction
           processes. <em>Wt1</em> regulates Sertoli and granulosa differentiation during gonad development by binding
           the <em>Sf-1</em> promoter [<a href="#_ENREF_49">
              
                 49
              </a>]. The inhibition of <em>CREB</em> could reduce oocyte meiotic resumption and cumulus cell expansion [<a href="#_ENREF_50">
              
                 50
              </a>]. Deshpande et al. [<a href="#_ENREF_51">
              
                 51
              </a>] reported that <em>Wnt2</em> might stimulate germ cells in male embryos to re-enter the cell cycle. <em>Nr5a1</em>/<em>SF-1</em> could bind the <em>Cyp19a1</em> promoter, which is crucial for functional maintenance of the ovary [<a href="#_ENREF_52">
              
                 52
              </a>]. <em>Zar1</em> is the first identified oocyte-specific maternal-effect gene that functions at the
           oocyte-to-embryo transition [<a href="#_ENREF_53">
              
                 53
              </a>]. Our results help to enrich the understanding of the reproductive regulation network
           in non-mammal vertebrates. </p>

In the present study, we identified mRNAs, miRNAs, and lncRNAs using high-throughput sequencing data from the ovary and testis of Chinese soft-shelled turtles and constructed the associated ceRNA networks. We identified 11 319 DEmRNAs, 633 DEmiRNAs, and 10 495 DElncRNAs in the ovary and testis. Furthermore, we constructed ceRNA networks, which included DEmRNAs, DEmiRNAs, and DElncRNAs that regulated the reproduction of the turtle. The present study provides an invaluable resource for further studies on the molecular regulation of reproduction in turtles.

Sample collection and RNA isolation

All investigations in present study were performed according to the Animal Experimental Guidelines of the Ethical Committee of the University of China. Nine adult female turtles (body weight 600 ± 45 g, mean ± SD) and nine male turtles (body weight 750 ± 50 g), aged 24 months, and cultured in the same pond were sacrificed before their reproductive season. The testes and ovaries were obtained after slaughtering, and immediately stored at −80 °C. Total RNA was isolated from each gondal sample using TRIzol reagent (Invitrogen, USA). RNA concentration and quality were determined using Nanodrop, Qubit 2.0 and an Agilent 2100 bioanalyser. The higher quality RNA was stored at −80 °C for constructing the library. The male and female turtles were divided into three groups and the RNA from three turtles was pooled. Six miRNA and lncRNA libraries from testes (n = 3) and ovaries (n = 3) were constructed.

Library preparation and sequencing

Sequencing libraries of miRNA were constructed using2.5 ng of RNA per gonadal sample of turtles. The library was constructed following the manufacturer’s instruductions of NEB Next Ultra small RNA Sample Library Prep Kit for Illumina (NEB, USA). Briefly, the linkers were added by T4 RNA ligase 1 and 2. Then, target fragments were amplified by RT-PCR using synthesised first-strand cDNA as the template, and the livraries were isolated and constructed by polyacrylamide gel electrophoresis. All sequences were qualitied by detecting with Qubit 2.0 and trimmed with an Agilent 2100 bioanalyser. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v4-cBot-HS (Illumina, USA). After that, the prepared libraries were sequenced on an Illumina HiSeq 2500 platform and paired-end reads were generated. The sequence was read by X-ten with single-end 50 nt.

Sequencing libraries of mRNA and lncRNA were constructed using 1.5 µg of RNA per gonadal sample which was removed rRNA by the Ribo-Zero rRNA Removal Kit (Epicentre, Madison, WI, USA). The libraries for sequencing were constructed using NEBNext^R Ultra^TM Directional RNA Library Prep Kit (NEB, Ipswich, MA, USA) for IlluminaR (NEB, USA) following the manufacturer’s recommendations. Briefly, first-strand cDNA and second-strand cDNA synthesis and the library fragments was purified by AMPure XP Beads (Beckman Coulter, Beverly, MA, USA). After that, the fragments with 150–200 base pairs (bp) in length were selected. The strands containing U base were removed by USER Enzyme (NEB, USA). PCR was then performed and the library quality was evaluate on Agilent Bioanalyser 2100. TruSeq PE Cluster Kitv3-cBot-HS (Illumina) was used to clustered on acBot Cluster Generation System according to the manufacturer’s instructions. The library preparations were then sequenced on an Illumina HiSeq platform and generating the paired-end reads.

Quality control

Quality control of the reads was performed using FastQC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The raw data were first processed by in-house perl scripts. Clean reads were obtained by discarding reads containing adapter and ploy-N, and low-quality reads. The Q20, Q30 and GC content of the filtered reads were calculated and were used for further analysis.

Transcriptome assembly

The clean reads were mapped to the turtle genome v1.0 (PelSin _1.0, NCBI) using HISAT2 [

                 54
              </a>] and Bowtie software [<a href="#_ENREF_55">
              
                 55
              </a>]. The mapped reads of each sample were assembled using String Tie [<a href="#_ENREF_56">
              
                 56
              </a>] according to the reference-based approach [<a href="#_ENREF_57">
              
                 57
              </a>]. For identified miRNA, the assembled transcripts were compared with ncRNAs (rRNA,
           tRAN, snRNA, snoRNA, and other ncRNAs) and repeats using Bowtie software [<a href="#_ENREF_55">
              
                 55
              </a>]. </p>

Prediction of miRNAs and lncRNAs targets

The mapped reads were aligned to miRbase (http://www.mirbase.org/) [

                 58
              </a>] using miRDeep2 [<a href="#_ENREF_59">
              
                 59
              </a>]. The characteristic hairpin structure of miRNA precursors were used to predict novel
           miRNA. The miRDeep2 [<a href="#_ENREF_59">
              
                 59
              </a>] and Mfold software were used for predicting the structure of the unannotated miRNAs
           and their precursors. </p>

After mapping the genome, the transcripts were screened for mRNA and lncRNAs. The transcripts with more than 200 nucleotides or that had more than one exon were selected as lncRNA candidates. The candidate lncRNAs were further screened by CPC/CNCI/Pfam/CPAT which could distinguish the protein-coding genes from the non-coding genes.

To annotate the function of the lncRNAs, we predicted the target protein coding genes of lncRNAs in cis and trans. The protein coding genes ranging from 100 kb upstream to 100 kb downstream of a lncRNA were identified as cis-acting target genes. The genes that overlapped with the lncRNAs predicted by LncTar [

                 60
              </a>] were annotated as <em>trans</em> acting target genes. </p>

Expression analysis

The expression levels of miRNAs in each sample were calculated and normalized using transcripts per million. The expression levels of protein-coding genes and lncRNAs were calculated as fragments per kilo base of exon per million fragments mapped and assessed using Cuffdiff (v2.1.1) [

                 61
              </a>]. Differential expression gnes (DEGs) analysis of the miRNAs, lncRNAs, and protein-coding
           genes were identified using the DEGseq package (1.10.1) [<a href="#_ENREF_62">
              
                 62
              </a>]. A false discovery rate (FDR) ≤ 0.01 and an absolute value of log2 (fold change,
           FC) ≥ 1 were determined as DEG miRNAs. LncRNAs and protein-coding genes with FDR &lt;
           0.01 and an absolute value of log2 (FC) ≥ 2 were determined as differentially expressed.</p>

Target gene prediction between miRNA and lncRNA

The prediction of interactions between miRNA and lncRNA was determined using miRanda. The prediction of target genes of differentially expressed miRNAs were identified using miRanda [

                 63
              </a>] and RNAhybrid [<a href="#_ENREF_64">
              
                 64
              </a>]. </p>

Bioinformatics analysis

For predicting the function of the miRNAs and lncRNAs, the target genes and differentially expressed genes were annotated against the NCBI non-redundant protein database (Nr), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and clusters of orthologous groups of proteins. GO terms with KS ≤0.05 and pathways with corrected P ≤0.05 were defined as significantly enriched.

Quantitative real-time PCR analysis of miRNA and lncRNA

In present study, 8 miRNAs and 7 lncRNAs were verified by using quantitative real-time PCR (qRT-PCR ). The primers for miRNA and lncRNA were shown in Additonal file 13. Total RNA of ovaries and testis was extracted by TRIzol reagent (Invitrogen, USA) strictly followed by the manufacturer’s instruction. Using total RNA from the testis and ovary of turtles as a template, first-strand cDNAs of miRNAs were obtained with a Mir-X miRNA first strand synthesis kit (Clontech, USA). Expression profiles of miRNAs were examined by SYBR qRT-PCR (Clontech). All reactions were performed in CFX96 Touch^TM (Bio-Rad, USA).

All experiments were repeated at least three times. Results of the qRT-PCR data are presented as the mean ± standard error of the mean value. Statistical analyses were performed using the SPSS 16.0 software program (SPSS Inc., Chicago, IL, USA). Differences were considered statistically significant at P <0.05.

CeRNA network construction

To construct the lncRNA–miRNA–mRNA network, we predicted lncRNAs that might act as endogenous sponges based on lncRNAs which were up- or down-regulated by an FC > 2.0 and P <0.05 that significantly correlated with the miRNA predicted target genes. LncRNAs that possessed miRNAs response elements as predicted by RNA22( http://cm.jefferson.edu/rna22/Precomputed) and PITA (http://genie.weizmann.ac.il/pubs/mir07/mir07_data.html).

Additiaonal files

Additional file 1: The results of reads mapped to the genome of Pelodiscus sinensis.

Additional file 2: The differently expressed miRNAs between ovaries and testes

Additional file 3: The predicted families of differentially expressed miRNAs

Additional file 4: The differentially expressed lncRNAs between ovaries and testes

Additional file 5: The differentially expressed mRNAs between ovaries and testes

Additional file 6 Gene Ontology (GO) analysis of differentially expressed miRNAs

Additional file 7 Gene Ontology (GO) analysis of differentially expressed lncRNAs

Additional file 8 Gene Ontology (GO) analysis of differentially expressed mRNAs

Additional file 9 The KEGG pathway analysis of DEmiRNAs

Additional file 10: The KEGG pathway analysis of DElncRNAs in cis

Additional file 11: The KEGG pathway of DElncRNAs in trans

Additional file 12: ceRNA networks results

Additional file 13: Primers used in qRT-PCR

Acknowledgements

We thank all the people who contributed to the present study.

Funding

This study was supported by the National Natural Science Foundation of China (31672640 and 31702316) , the Open Program of Key Laboratory of Cultivation and High-value Utilization of Marine Organisms in Fujian Province (2018fjscq04 and 2018fjscq05).

Ethics approval

The Chinese soft-shelled turtle in this study were obtained from the aquaculture base of Henan Normal University and culture in our own laboratory, and all turtles were anesthetized with 2-phenxyethanol before being euhanized. The fish were cared for according to the Regulations for the Administration of Affairs Concerning Experimental Animals for the Science and Technology Bureau of China throughout the study, and the experimental protocols were approved by the Animal Management Committee of Henan Provincial Laboratory.

Consent for publication

No applicable.

Competing interests

The authors declare that they have no competing interests.

Competing interests

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details

¹ College of Fisheries, Henan Normal University, Xinxiang, Henan 453007, People´s Republic of China.²College of Animal Science and Technology, Hunan Agriculture University, Changsha, Hunan 410128, People´s Republic of China.³ Fisheries Research Institute of Fujian, Xiamen, Fujian 361000, People´s Republic of China.

1. Zhang J, Yu P, Zhou Q, Li X, Ding S, Su S, Zhang X, Yang X, Zhou W, Wan QGui JF. Screening and characterisation of sex differentiation-related long non-coding RNAs in Chinese soft-shell turtle (Pelodiscus sinensis). Sci Rep. 2018, 8(1):8630.

2. Liu T, Yang P, Chen H, Huang Y, Liu Y, Waqas Y, Ahmed N, Chu X, Chen Q. Global analysis of differential gene expression related to long-term sperm storage in oviduct of Chinese Soft-Shelled Turtle Pelodiscus sinensis. Sci Rep. 2016, 6:33296.

3. Pearse DE AJ. Turtle mating systems behavior, sperm storage, and genetic paternity. J Hered. 2001, 92:206-211.

4. Liu G, Mattick JS, Taft RJ. A meta-analysis of the genomic and transcriptomic composition of complex life. Cell Cycle. 2013, 12(13):2061-2072.

5. Skaftnesmo KO, Edvardsen RB, Furmanek T, Crespo D, Andersson E, Kleppe L, Taranger GL, Bogerd J, Schulz RW, Wargelius A. Integrative testis transcriptome analysis reveals differentially expressed miRNAs and their mRNA targets during early puberty in Atlantic salmon. BMC Genomics. 2017, 18(1):801.

6. Yang H, Wang F, Li F, Ren C, Pang J, Wan Y, Wang Z, Feng X, Zhang Y. Comprehensive Analysis of long non-coding RNA and mRNA Expression Patterns in Sheep Testicular Maturation. Biol of Reprod. 2018:650-661.

7. Esteller M. Non-coding RNAs in human disease. Nat Rev Genet. 2011, 12(12):861-874.

8. Nithin C, Thomas A, Basak J, Bahadur RP. Genome-wide identification of miRNAs and lncRNAs in Cajanus cajan. BMC Genomics. 2017, 18(1):878.

9. Kumar A, Wong AK, Tizard ML, Moore RJ, Lefevre C. miRNA_Targets: a database for miRNA target predictions in coding and non-coding regions of mRNAs. Genomics. 2012, 100(6):352-356.

10. Ernst EH, Nielsen J, Ipsen MB, Villesen P, Lykke-Hartmann K. Transcriptome Analysis of Long Non-coding RNAs and Genes Encoding Paraspeckle Proteins During Human Ovarian Follicle Development. Front Cell Dev Biol. 2018, 6:78.

11. Lin X, Han M, Cheng L, Chen J, Zhang Z, Shen T, Wang M, Wen B, Ni T, Han C. Expression dynamics, relationships, and transcriptional regulations of diverse transcripts in mouse spermatogenic cells. RNA Biol. 2016, 13(10):1011-1024.

12. Tesfaye D, Gebremedhn S, Salilew-Wondim D, Hailay T, Hoelker M, Grosse-Brinkhaus C, Schellander K. MicroRNAs: tiny molecules with a significant role in mammalian follicular and oocyte development. Reproduction. 2018, 155(3):R121-R135.

13. Yang J, Li X, Cao YH, Pokharel K, Hu XJ, Chen ZH, Xu SS, Peippo J, Honkatukia M, Kantanen J. Comparative mRNA and miRNA expression in European mouflon (Ovis musimon) and sheep (Ovis aries) provides novel insights into the genetic mechanisms for female reproductive success. Heredity. 2019, 122(2):172-186.

14. Wang F, Jia Y, Wang P, Yang Q, Du Q, Chang Z. Identification and profiling of Cyprinus carpio microRNAs during ovary differentiation by deep sequencing. BMC Genomics. 2017, 18(1):333.

15. Giraldez AJ, Mishima Y, Rihel J, Grocock RJ, Van Dongen S, Inoue K, Enright AJ. Zebrafish MiR-430 Promotes deadenylation and clearance of maternal mRNAs. Science. 2006, 312:75-79.

16. Gao Y, Jia D, Hu Q, Li D. Foxl3, a Target of miR-9, stimulates spermatogenesis in spermatogonia during natural sex change in Monopterus albus. Endocrinology. 2016, 157(11):4388-4399.

17. Gu Y, Zhang L, Chen X. Differential expression analysis of Paralichthys olivaceus microRNAs in adult ovary and testis by deep sequencing. Gen Comp Endocr. 2014, 204:181-184.

18. de Mateo S, Sassone-Corsi P. Regulation of spermatogenesis by small non-coding RNAs: role of the germ granule. Semin Cell Dev Biol. 2014, 29:84-92.

19. McIver SC, Roman SD, Nixon B, McLaughlin EA. miRNA and mammalian male germ cells. Hum Reprod Update.2012, 18(1):44-59.

20. Taylor DH, Chu ET, Spektor R, Soloway PD. Long non-coding RNA regulation of reproduction and development. Mol Reprod Dev.2015, 82(12):932-956.

21. Kato Y, Perez CAG, Mohamad Ishak NS, Nong QD, Sudo Y, Matsuura T, Wada T, Watanabe H. A 5' UTR-overlapping lncRNA activates the male-determining gene doublesex1 in the crustacean Daphnia magna. Curr Biol. 2018, 28(11):1811-1817 e1814.

22. Arun G, Akhade VS, Donakonda S, Rao MR. mrhl RNA, a long noncoding RNA, negatively regulates Wnt signaling through its protein partner Ddx5/p68 in mouse spermatogonial cells. Molecular and Cellular Biology. 2012, 32(15):3140-3152.

23. Wen K, Yang L, Xiong T, Di C, Ma D, Wu M, Xue Z, Zhang X, Long L, Zhang W, Zhang J, Bi X, Dai J, Zhang Q, Lu ZJ, Gao G. Critical roles of long noncoding RNAs in Drosophila spermatogenesis. Genome Res.2016, 26(9):1233-1244.

24. Akhade VS, Arun G, Donakonda S, Rao MR. Genome wide chromatin occupancy of mrhl RNA and its role in gene regulation in mouse spermatogonial cells. RNA Biol. 2014, 11(10):1262-1279.

25. Kataruka S, Akhade VS, Kayyar B, Rao MRS. Mrhl Long Noncoding RNA mediates meiotic commitment of mouse spermatogonial cells by regulating Sox8 expression. Mol Cell Biol.2017, 37(14):e00632.

26. Qin C, Xia X, Fan Y, Jiang Y, Chen Y, Zhang N, Uslu B, Johnson J, Kallen AN. A novel, noncoding-RNA-mediated, post-transcriptional mechanism of AMH regulation by the H19/let-7 axis. Biol Reprod.2019, 100(1):101-111.

27. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the rosetta stone of a hidden RNA language? Cell.2011, 146(3):353-358.

28. Lin P, Wen DY, Li Q, He Y, Yang H, Chen G. Genome-wide analysis of prognostic lncRNAs, miRNAs, and mRNAs forming a competing endogenous RNA network in hepatocellular carcinoma. Cell Physiol Biochem.2018, 48(5):1953-1967.

29. Li L, Huang C, He Y, Sang Z, Liu G, Dai H. Knockdown of long non-coding RNA GAS5 increases miR-23a by targeting ATG3 involved in autophagy and cell viability. Cell Physiol Biochem. 2018, 48(4):1723-1734.

30. Huang Y, Ren HT, Wang ZB, Sun XH. Identifcation and validation validation of novel microRNA molecule from the Pelodicus sinensis by bioinformatics approaches. Bioorganicheskaia Khimiia.2015, 41(4):416-426.

31. Wu F, Liu Y, Wu Q, Li D, Zhang L, Wu X, Wang R, Zhang D, Gao S, Li W. Long non-coding RNAs potentially function synergistically in the cellular reprogramming of SCNT embryos. BMC Genomics2018, 19(1):631.

32.Carrington JC, Ambros V. Role of microRNAs in plant and animal development. Science.2003, 301(5631):336-338.

33. Golicz AA, Bhalla PL, Singh MB. lncRNAs in plant and animal sexual reproduction. Trends in Plant Sci.2018, 23(3):195-205.

34. Yang C, Yao C, Tian R, Zhu Z, Zhao L, Li P, Chen H, Huang Y, Zhi E, Gong Y Xue Y, Wang H, Yuan Q, He Z, Li Z. miR-202-3p regulates sertoli cell proliferation, synthesis function, and apoptosis by targeting LRP6 and cyclin D1 of Wnt/beta-catenin signaling. Mol Ther Nucl Acids.2018, 14:1-19.

35. Sun XF, Li YP, Pan B, Wang YF, Li J, Shen W. Molecular regulation of miR-378 on the development of mouse follicle and the maturation of oocyte in vivo. Cell Cycle.2018:1-13.

36. Pan B, Toms D, Shen W, Li J. MicroRNA-378 regulates oocyte maturation via the suppression of aromatase in porcine cumulus cells. Am J Physiol Endoc M.2015, 308(6):E525-534.

37. Sinha PB, Tesfaye D, Rings F, Hossien M, Hoelker M, Held E, Neuhoff C, Tholen E, Schellander K, Salilew-Wondim D. MicroRNA-130b is involved in bovine granulosa and cumulus cells function, oocyte maturation and blastocyst formation. J Ovarian Res.2017, 10(1):37.

38. He Z, Jiang J, Kokkinaki M, Tang L, Zeng W, Gallicano I, Dobrinski I, Dym M. MiRNA-20 and mirna-106a regulate spermatogonial stem cell renewal at the post-transcriptional level via targeting STAT3 and Ccnd1. Stem Cells.2013, 31(10):2205-2217.

39. Cui N, Hao G, Zhao Z, Wang F, Cao J, Yang A. MicroRNA-224 regulates self-renewal of mouse spermatogonial stem cells via targeting DMRT1. J Cell Mol Med.2016, 20(8):1503-1512.

40. Li C, Yang B, Pan P, Ma Q, Wu Y, Zhang Z, Guo X, Ye J, Gui Y. MicroRNA-130a inhibits spermatogenesis by directly targeting androgen receptor in mouse sertoli cells. Mol Reprod Dev. 2018, 85(10):768-777.

41. Hayashi K, Chuva de Sousa Lopes SM, Kaneda M, Tang F, Hajkova P, Lao K, O'Carroll D, Das PP, Tarakhovsky A, Miska EA, Surani MA. MicroRNA biogenesis is required for mouse primordial germ cell development and spermatogenesis. PloS One.2008, 3(3):e1738.

42. Jung YH, Gupta MK, Shin JY, Uhm SJ, Lee HT: MicroRNA signature in testes-derived male germ-line stem cells. Mol Hum Reprod.2010, 16(11):804-810.

43. Bohmdorfer G, Wierzbicki AT. Control of Chromatin Structure by Long Noncoding RNA. Trends Cell Biol.2015, 25(10):623-632.

44. Lei Q, Pan Q, Li N, Zhou Z, Zhang J, He X, Peng S, Li G, Sidhu K, Chen S, Hua J. H19 regulates the proliferation of bovine male germline stem cells via IGF-1 signaling pathway. J Cell Physiol.2018, 234(1):915-926.

45. Matsuzaki H, Okamura E, Shimotsuma M, Fukamizu A, Tanimoto K. A randomly integrated transgenic H19 imprinting control region acquires methylation imprinting independently of its establishment in germ cells. Mol Cell Biol. 2009, 29(17):4595-4603.

46. Hosono Y, Niknafs YS, Prensner JR, Iyer MK, Dhanasekaran SM, Mehra R, Pitchiaya S, Tien J, Escara-Wilke J, Poliakov A, Chu SC, Saleh S, Sankar K, Su F, Guo S, Qiao Y, Freier SM, Bui HH, Cao X, Malik R, Johnson TM, Beer DG, Feng FY, Zhou W, Chinnaiyan AM. Oncogenic Role of THOR , a conserved Cancer/Testis long non-coding RNA. Cell.2017, 171(7):1559-1572.e20.

47. Necsulea A, Soumillon M, Warnefors M, Liechti A, Daish T, Zeller U, Baker JC, Grutzner F, Kaessmann H. The evolution of lncRNA repertoires and expression patterns in tetrapods. Nature. 2014, 505(7485):635-640.

48. Paneru B, Ali A, Al-Tobasei R, Kenney B, Salem M. Crosstalk among lncRNAs, microRNAs and mRNAs in the muscle 'degradome' of rainbow trout. Sci Rep. 2018, 8(1):8416.

49. Chen M, Zhang L, Cui X, Lin X, Li Y, Wang Y, Wang Y, Qin Y, Chen D, Han CZhou B, Huff V, Gao F. Wt1 directs the lineage specification of sertoli and granulosa cells by repressing Sf1 expression. Development. 2017, 144(1):44-53.

50. Wang Y, Hao X, Yang J, Li J, Zhang M. CREB activity is required for luteinizing hormone-induced the expression of EGF-like factors. Mol Reprod and Dev. 2016, 83(12):1116-1127.

51. Deshpande G NA, Schedl P. Wnt signaling in sexual dimorphism. Genetics. 2016, 202:661-673.

52. Wang J, Gong Y. Transcription of CYP19A1 is directly regulated by SF-1 in the theca cells of ovary follicles in chicken. Gen Comp Endocr.2017, 247:1-7.

53. Wu X, Viveiros MM, Eppig JJ, Bai Y, Fitzpatrick SL, Matzuk MM. Zygote arrest 1 (Zar1) is a novel maternal-effect gene critical for the oocyte-to-embryo transition. Nat Genet. 2003, 33(2):187-191.

54. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015, 12(4):357-360.

55. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10(3):R25.

56. Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015, 33(3):290-295.

57. Pertea M, Kim D, Pertea GM, Leek JT, Salzberg SL. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016, 11(9):1650-1667.

58. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014, 42(Database issue):D68-73.

59. Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res.2012, 40(1):37-52.

60. Li J, Ma W, Zeng P, Wang J, Geng B, Yang J, Cui Q. LncTar: a tool for predicting the RNA targets of long noncoding RNAs. Brief Bioinform. 2015, 16(5):806-812.

61. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc.2012, 7(3):562-578.

62. Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010, 26(1):136-138.

63. Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA.org resource: targets and expression. Nucleic Acids Research. 2008, 36(Database issue):D149-153.

64. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004, 10(10):1507-1517.

Additional file 1: The results of reads mapped to the genome of Pelodiscus sinensis. Additional file 2: The differently expressed miRNAs between ovaries and testes Additional file 3: The predicted families of differentially expressed miRNAs Additional file 4: The differentially expressed lncRNAs between ovaries and testes Additional file 5: The differentially expressed mRNAs between ovaries and testes Additional file 6: Gene Ontology (GO) analysis of differentially expressed miRNAs Additional file 7: Gene Ontology (GO) analysis of differentially expressed lncRNAs in cis Additional file 8: Gene Ontology (GO) analysis of differentially expressed mRNAs in trans Additional file 9: The KEGG pathway analysis of DEmiRNAs Additional file 10: The KEGG pathway analysis of DElncRNAs in trans Additional file 11: The KEGG pathway of DElncRNAs in cis Additional file 12: ceRNA network result Additional file 13: Primers used in qRT-PCR

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Genome-wide identification and comparison of differentially expressed profiles of miRNAs and lncRNAs with associated ceRNA networks in the gonads of Chinese soft-shelled turtle, Pelodiscus sinensis

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Results

Overview of the sequencing data

Identification of differentional expression mRNAs, miRNAs, and lncRNAs

Functional analysis of DEmiRNAs and DElncRNAs

Validation of differentially expressed miRNAs and lncRNAs

Construction of ceRNA network

Discussion

Conclusions

Methods

Sample collection and RNA isolation

Library preparation and sequencing

Quality control

Transcriptome assembly

Prediction of miRNAs and lncRNAs targets

Expression analysis

Target gene prediction between miRNA and lncRNA

Bioinformatics analysis

Quantitative real-time PCR analysis of miRNA and lncRNA

CeRNA network construction

Additiaonal files

Declarations

Acknowledgements

Funding

Ethics approval

Consent for publication

Competing interests

Competing interests

Author details

References

Additional File legends

Supplementary Files

Status:

Journal Publication

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