Identification of lncRNA expression profiles associated with ovarian development and ageing process in mice

doi:10.21203/rs.3.rs-3199928/v1

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Identification of lncRNA expression profiles associated with ovarian development and ageing process in mice

https://doi.org/10.21203/rs.3.rs-3199928/v1

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Long non-coding RNA (lncRNA) participates in various biological processes, however, neither the expression profile nor the biological role of lncRNAs in mammalian ovaries has been fully studied. In this work, the lncRNA transcriptomic analysis of postnatal mice ovaries was performed by using bulk RNA sequencing in C57BL/6 mice. A total of 5,302 lncRNAs were found in mouse ovaries, and 1836 lncRNAs were differentially expressed during the development and ageing process, of which targets were enriched in the developmental process, reproduction, etc. Developmental stage specific lncRNAs showed functions in system development, inflammatory response, myeloid leukocyte activation, etc. Moreover, a co-expression network analysis based on reproduction-related genes reveals lncRNAs that may regulate multiple mRNA targets in ovaries, including Neat1, Gm11613 and Gm43915. Two cis-acting lncRNAs, Ptgs2os and Gm14705, showed correlated expression pattern with their potential targets Ptgs2 and Aff2 respectively, and these lncRNA-mRNA pairs were conserved in mice and humans. WGCNA further identified 10 co-expressed modules with distinct expression patterns associated with ovarian development and ageing. Taken together, our results reveal a transcriptomic profile of mouse ovaries over the reproductive lifespan, providing insights into the molecular mechanisms of ovarian development and ageing.

long non-coding RNA

ovarian development

premature ovarian insufficiency

WGCNA

As an important part of the female reproductive system, ovaries have two main functions. They produce eggs for fertilization and secrete hormones that play roles in the menstrual cycle and fertility(Colvin and Abdullatif 2013). Follicles are the basic functional unit of oocyte genesis and development(Gougeon 1996). Primordial follicles show meiosis arrest at the diplotene stage of prophase I, which may last for several months (in mice) or even up to 50 years (in humans) until follicular formation is activated(Piprek 2016). The gradual growth of follicles requires the coordinated development of oocytes and granulosa cells. Oocytes capable of fertilization are ovulated, while antral follicles that fail to ovulate eventually become atretic. Importantly, female mammals have a limited ovarian reserve which does not increase during adulthood. From birth, the number of primordial follicles in the ovary decreases steadily until the follicles are exhausted, which leads to menopause and infertility(Findlay et al. 2015). Menopause usually occurs in healthy female around 50 years old. However, internal and external stimulations could both have an impact on the limited resting follicles, thus impairing follicular development and female reproduction.

Long non-coding RNAs (lncRNAs) are RNAs over 200 nt that are not translated into proteins. Increasing evidence suggests that lncRNA plays crucial roles in differentiation and development in animals and plants, with additional roles in a broad range of physiological processes such as DNA damage response, glucose metabolism, inflammation and so on(Mattick et al. 2023). Usually, most lncRNAs exhibit restricted expression patterns at lower levels than mRNAs. In 2018, Hong et al found that lncRNAs were most abundantly expressed in mouse testes as compared to other organs such as brain, kidney and heart, suggesting the essential function of lncRNA in mammalian reproduction(Hong et al. 2018). Consistently, Rolland et al identified a core group of 113 lncRNAs and 20 novel genes, which were dynamically and syntonically transcribed during spermatogenesis using RNA sequencing analysis in human testicular cells(Rolland et al. 2019). Zhang et al. reported that several lncRNAs including lnc32058, lnc09522 and lnc98487 were upregulated in asthenozoospermic sperm, providing insights into the causes of male infertility(Zhang et al. 2019). Although there have been extensive studies of lncRNAs on mammalian spermatogenesis, the expression patterns of lncRNAs in female reproductive tissues remain to be clarified, especially in gene expression profiling with higher temporal resolution over the whole female reproductive lifespan.

In this study, we performed transcriptome sequencing analysis of lncRNA in the mouse ovaries at different developmental stages. Differentially expressed lncRNAs markers were identified among developmental stages and a co-expression network was built based on the lncRNAs and their predicted targets. Weighted correlation network analysis (WGCNA) found important lncRNA clusters that exhibited time-related expression patterns. Our work first profiled the lncRNA expression patterns during mouse ovary development and ageing, and explored their potential functions.

RNA sequencing and data processing

RNA samples were extracted from ovaries of C57BL/6 mice at different developmental stages in the previous work(Zhou et al. 2021). Ovaries were collected at each time point, and three high-quality RNA samples were selected for sequencing. The RNA-seq data went through the same quality control and alignment steps as the previous work. In brief, paired reads were trimmed by Trimmomatic and mapped to GRCm38/mm10 reference genome by Tophat2. The raw sequence data are available under accession number CRA003645 that is publicly accessible at https://bigd.big.ac.cn/gsa. Raw gene counts were called from uniquely mapped reads through featureCounts strand-specific read counting(Liao et al. 2014), with gencode.vM19.annotation.gtf file as the gene annotation file. LncRNAs and protein-coding mRNAs with read counts greater than 10 in at least two samples were considered as expressed genes and retained in the ovary expression matrix for subsequent analysis.

Identification of differentially expressed genes

Differentially expressed protein-coding and lncRNA genes (DEGs) were identified between each pair of adjacent time points using the R package DESeq2 (FDR < 0.05; |log₂(fold change)| > 1)(Love et al. 2014). Similarly, stage-specifically enriched lncRNAs were identified in the target stage against all the other stages (FDR < 0.05; fold change (FC) of normalized read counts > 2). DEGs were visualized in heatmap by using R pheatmap package.

LncRNA regulatory targets prediction

The targets of differentially expressed lncRNAs were predicted by evaluating cis/trans-regulatory effects. mRNAs located within 10 kbp upstream or downstream of their paired lncRNAs were considered cis-target genes. Targets of Trans-acting lncRNAs were predicted based on mRNA sequence complementarity using BLAST software (e-value < 1e-20). The most likely trans-acting target genes were selected by RNAplex software with RNA duplex energy parameter ≤ − 30.

Gene ontology (GO) enrichment analysis

To annotate the biological pathways associated with lncRNAs, GO enrichment analysis was performed using the predicted target genes of lncRNAs. The R package clusterProfiler was utilized based on org.Mm.eg.db database with Q-value cut-off < 0.05 applied. Only GO terms belonging to the "biological process" category were included.

Co-expression network

Furthermore, we investigated whether target candidates of lncRNAs show co-expression with the putatively regulatory lncRNA, using a Pearson correlation coefficient (P value < 0.01). Based on research interests, mRNAs enriched in reproduction ontology as well as lncRNAs predicted to regulate them were used to construct the co-expression network, including both positive and negative correlations. Co-expression relationships between lncRNA-mRNA regulatory pairs were visualized by Cytoscape, excluding those within the same RNA type (lncRNA-lncRNA and mRNA-mRNA). Minor adjustments were made for a concise layout.

Weighted correlation network analysis (WGCNA)

WGCNA package in R(Langfelder and Horvath 2008) was applied to find clusters (modules) of highly correlated genes. Identified clusters can be summarized by the module eigengene or an intramodular hub gene. According to the tutorial document, a signed network was constructed and Power was decided as 9 based on scale independence and mean connectivity. The association between gene modules and ovarian development can be shown by relating module eigengene adjacency with time. The representative genes of each module were selected among the top 10 genes based on the Pearson correlation with module eigengene in each module.

Cluster-cluster correlation between lncRNA modules and mRNA clusters

A cluster-cluster correlation was calculated to describe the similar expression pattern between groups of lncRNAs and mRNAs. The Pearson correlation coefficient was calculated by the eigengene of each WGCNA module and mean gene expression of previously defined mRNA clusters (Zhou et al. 2021) along with time. A heatmap with hierarchical clustering was applied to visualize the correlation coefficient.

Statistical analysis

All statistical analysis was performed in R 3.6.1. All adjusted P values or FDRs from multiple test correction in this work were based on Benjaminiand-Hochberg correction. DEG analysis was conducted by negative binomial GLM fitting and Wald test, the default test in DESeq2 package. Test of co-expression was based on Fisher’s Z transform when calculating Pearson correlation coefficient. When comparing the expression level of representative genes in WGCNA modules, "Least significant difference" and adjusted P-values were calculated by LSD.test in agricolae R package. The significant thresholds were 0.05 except the one in co-expression tests being 0.01.

Identification of lncRNAs in mouse ovaries

In our previous study, seven groups of ovaries from newborn mice and mice at 3 weeks, 2 months, 3 months, 6 months, 9 months and 1 year of age were harvested and subjected to bulk RNA sequencing (Zhou et al. 2021). Principal component analysis on RNA sequencing data further defined four stages during the mouse ovarian lifespan: the neonatal stage (NN) containing the 0-day group, the prepubertal stage (PPB) containing the 3-week group, the pubertal stage (PB) containing the 2- and 3-month groups and the sexually mature or aged stage (MA) containing the 6-month, 9-month and 1-year groups. To investigate the dynamic changes of lncRNA through ovarian maturation and ageing, we reanalyzed the RNA sequencing data (Fig. 1a, b). The raw data were first aligned to mm10 mouse reference genome after quality control. Read counts for both mRNA and lncRNAs were calculated to investigate their expression pattern, predict regulatory targets of lncRNAs, annotate their functions and identify co-expressed lncRNA clusters. We also built lncRNA-mRNA co-expression network based on some crucial genes involved in ovarian development and ageing.

We found 5,302 lncRNAs expressed in mice ovaries, most of which were found to be located on Chr5, while only 58 lncRNAs were found to be located on Chr18 (Fig. 2a). Additionally, 83 expressed lncRNAs were called from the sex chromosome. We did not find any obvious correlation between the length of chromosomes and the number of expressed lncRNAs (Fig. 2b). The mean exon length was found to be 2,224bp and the median 1,796 bp. LncRNAs of 200–1000 bp and 1000–2000 bp were found to consist of 27% and 28% respectively, while there were only less than 1% lncRNAs exceeding 10,000 bp. The expressed lncRNAs were split into 8 categories according to Ensembl database (Fig. 2c). Large intergenic non-coding RNA (lincRNA) and antisense RNA (asRNA) consisted of about 31.74% and 20.55% respectively. Only 2 lncRNAs were categorized into 3 prime overlapping ncRNAs.

Functional annotation of the differentially expressed lncRNAs in mouse ovarian development and ageing

To identify the differentially expressed lncRNAs, we compared the transcriptional level of lncRNAs between every adjacent time point, and 1836 lncRNAs were identified over the reproductive lifespan. As shown in Fig. 3a, we found 1504 differentially expressed lncRNAs in the 3-week stage as compared to that in the 0-day stage, suggesting crucial roles of lncRNA in ovarian development. The next most differentially expressed lncRNAs were between 3 weeks and 9 weeks, with a total of 382 down-regulated lncRNAs and only 182 up-regulated lncRNAs. While we found little difference between the 9-week and 12-week groups, 53 differentially expressed lncRNAs were identified from ovaries of 12-week age to 53-week age, indicating their potential functions in regulating ovarian maturation and ageing.

Next, GO analysis was performed on all differentially expressed lncRNAs to achieve a general inspection of biological functions. We predicted their possible target mRNAs for regulation, and 10,779 lncRNA-mRNA regulatory pairs were finally obtained. Among them, 668 pairs of lncRNA-mRNA were predicted by cis-regulation, and 10248 pairs of lncRNA-mRNA were predicted by trans regulation. LncRNAs were then annotated through GO enrichment analysis based on their predicted mRNA targets. As shown in Fig. 3b, the general development terms including developmental process, system development and regulation of developmental process, were of the most significance, with adjusted P-value less than 1E-10. Notably, several terms related to reproduction were also of statistical significance, including reproduction (P_adjusted=0.0008), the developmental process involved in reproduction (P_adjusted=0.002), and sex differentiation (P_adjusted=1.59×10^− 5).

To better understand how state-specific lncRNAs chronologically express during ovarian development, up-regulated lncRNAs with |log₂FC| > 1 for each stage were shown in the heatmap (Fig. 3c). The NN group had the greatest number of specifically expressed lncRNAs (n = 962), and the top 3 lncRNAs were Gm45765, H19 and Gm44601. There were 308, 25 and 254 stage-specific enriched lncRNAs recognized in PPB, PB and MA, respectively. Notedly, the expression patterns of NN and PPB samples were highly consistent among replicates. However, this uniformity gradually weakened in the PB and MA stage, suggesting that individual differences could become larger during ovarian ageing.

Next, GO enrichment analysis was performed to further analyze the function of stage-specific expressed lncRNAs based on their predicted mRNA targets (Fig. 3d). Not surprisingly, NN-specific lncRNAs were overrepresented in pathways associated with early developmental processes, including structure morphogenesis, single-multicellular organism process and multicellular organism development. However, most GO terms enriched in PPB and MA stages were associated with immune function, such as inflammatory response, defense response and myeloid leukocyte activation. These findings suggested that lncRNAs might regulate ovarian maturation and ageing by regulating immune system.

Identification of conserved lncRNA regulatory pairs between mouse and human

A co-expression network was further constructed based on the significant regulatory lncRNA-mRNA pairs in GO reproduction term. After filtering with P < 0.01 for the Pearson correlation coefficient, we finally acquired a network of 188 lncRNA-mRNA pairs including 52 lncRNAs and 58 mRNAs (Fig. 4, Fig. S1), most of which were found to be positively co-expressed. As shown in Fig. 4, several important mRNA nodes were identified. For example, Six4, a transcription factor regulating sex differentiation and ovarian development is found in the center, with dozens of lncRNAs being co-expressed(Stévant and Nef 2019). Another important mRNA node is Tdrkh, which forms a complex in mammalian oocytes and is essential to fertility, with non-coding RNA getting involved(Tan et al. 2020). On the other hand, lncRNAs such as Neat1, Gm11613, Gm43915 and Gm42653 were found to have the ability to regulate multiple reproductive-related genes, indicating that the expression of these lncRNAs could play an important role in maintaining reproductive function. Besides, a considerable number of lncRNA and mRNA were found to occur in pairs rather than in network, indicating a specific regulation pattern, especially for those cis-regulatory pairs (Fig. S1).

In human, abnormality in ovarian development and ageing often contribute to ovarian diseases. Thus, we focused on lncRNAs that are conserved between mice and human, and several conserved regulatory pairs in the co-expression network raised our interests. For example, Ptgs2os is located at the opposite strain of the promoter region of Ptgs2 (Fig. 5a). The homologous lncRNA of Ptgs2os in human genome is known as PACER (Geng and Tan 2016), which is also an antisense RNA that overlaps with the Ptgs2 promoter region on the opposite chain of PTGS2. The expression level of Ptgs2os is positively correlated with Ptgs2 (R = 0.61, P < 0.01)(Fig. 5b), which is consistent with previous findings in human (Krawczyk and Emerson 2014). We also looked for lncRNA-mRNA regulatory pairs in the known pathogenic genes of human ovary diseases such as POI (Tucker et al. 2016). As shown in Fig. 5c, lncRNA Gm14705 was found to be located in the opposite chain of its predicted target Aff2 and overlapped with the promoter region of Aff2 gene. There was a strong correlation (R = 0.83, P < 0.001) between the expression levels of Gm14705 and Aff2 (Fig. 5d). Since the pathogenic variant of AFF2 could cause female infertility, further investigation of the interaction between Gm14705 and Aff2 is helpful to understand the underlying mechanism of AFF2 in regulating ovarian function.

Co-expressed modules of lncRNAs in mouse ovarian development and ageing

WGCNA was performed to further characterize the co-expression modules of lncRNAs during ovarian development and ageing. Soft threshold was decided by scale free topology model fit signed R² (Fig. S2), and 1836 lncRNAs were grouped into 10 modules (MEs), which ranged in size from 35 to 1274 lncRNA genes per module (Fig. 6a, 6b, S3). The six major modules showing expression patterns associated with ovarian development include ME2 (blue n = 1157), ME3 (brown n = 598), ME4 (yellow n = 582), ME5 (green n = 433), ME7 (black n = 253) and ME8 (pink n = 221) and the expression pattern of their representative lncRNA genes were shown (Fig. 6b, 6c). ME2 showed specific expression at NN stage, with low expression levels at all other stages, indicating the essential role of these lncRNAs in the early ovarian development before birth. Interestingly, H19 was shown as a representative gene of ME2, which is one of the most highly expressed genes at NN stage identified in our previous DEG analysis.ME3 (brown) showed a strong positive linear correlation with time, which was also shown in the eigengene adjacency heatmap(Fig. 6b, S4), with Gm47547 as a representative lncRNA gene. ME5 (green) included lncRNAs specifically up-regulated in PPB, and Gm1070 was one representative gene. Opposite expression patterns were found in ME4 (yellow) and ME8 (pink), with remarkable changes when transiting from PPB to PB stage. All these lncRNA genes may be associated with the puberty and sexual maturity, such as E330017L17Rik and Gm45889. LncRNAs in ME7 (black) were first up-regulated in PPB and then kept decreasing during ovarian ageing process. One representative gene of ME7, Mir17hg, was specifically highly expressed at week 3. All these findings strongly suggest the potential functions of these lncRNA correlation networks in regulating mouse ovarian development and ageing.

The physiological ageing process of mammalian ovaries are under strict regulation. Once it is disturbed, ovarian dysfunction might arise, including early menopause and premature ovarian insufficiency (POI). POI refers to the loss of ovarian activity before 40 years of age, and is the main reason for female infertility(Coulam et al. 1986; van der Steeg et al. 2007; Shaw et al. 2010). It is reported that about 1–3% of women experience POI before 40(Coulam et al. 1986; Jacobsen et al. 2003). Although pathogenic variants in more than 100 POI causative genes have been reported, the etiology of many POI patients remains unknown due to high genetic heterogeneity.

In our previous work, the transcriptomic landscape of postnatal mouse ovarian development and ageing was constructed by utilizing a mouse model and bulk RNA sequencing. It is revealed that many POI causative/candidate genes exhibited specific expression patterns during the reproductive ageing process, providing a new strategy for identification of novel regulators. Neither the expression profile nor the biological function of lncRNA in mammalian ovaries has been fully studied, but evidence is accumulating to suggest that lncRNAs participate in ovary function maintenance. Loss of lncRNA HCP5 induced POI through regulating the expression of MSH5 and thus regulation of DNA damage repair pathways (Wang et al. 2020). Additionally, lncRNA GCAT1 is another novel POI causative gene(Li et al. 2021). LncRNA GCAT1 could regulate the proliferation of granulosa cells by regulating p27 translation via competitive binding to PTBP1. All these studies strongly suggest prospective in clinics to uncover the functions of lncRNAs in ovary development and ageing. In this study, we investigated the lncRNA expression patterns during mouse ovarian development and ageing, and 1836 differentially expressed lncRNAs in mouse ovaries were identified for the first time.

Stage-specific expressed lncRNA in ovaries throughout the reproductive lifespan was further identified in our study, and some lncRNAs were shown to be involved in the regulation of ovary development or ageing process. For example, H19 is abundantly expressed in the newborn mouse ovaries (Fig. 3c, 6c). There has been evidence to suggest that H19 is associated with the risk of polycystic ovary syndrome and male infertility(Li et al. 2016; Ghasemi et al. 2020). Furthermore, female H19 knockout mice have been shown to exhibit accelerated follicular recruitment and atresia and a more rapid decline in fertility as compared to wild type mice (Xia et al. 2020). Similarly, Mir17hg, which was shown to be highly expressed at week 3, and with expression level gradually decreasing after that, had a human homologue, MIR17HG. MIR17HG was revealed to be in a positive feedback loop with SIRT1, promoting the repair of DNA double-stranded breaks in oocytes (Xie et al. 2019).

A lncRNA-mRNA coexpression network was also established in our studies to identify candidate lncRNA-mRNA pairs involved in the gene regulation throughout the reproductive lifespan. For example, Neat1 (Fig. 4), which was predicted to regulate expression of more than one gene associated with reproduction in the co-expression network, has been reported to be associated with the formation of paraspeckles, which are specifically formed in large quantities during follicular development (Ernst et al. 2018). Knocking NEAT1 down has been shown to impair the formation of paraspeckles (Chen and Carmichael 2009; Clemson et al. 2009; Sasaki et al. 2009). It will be fascinating in the near future to find out the target genes of Neat1 and how it regulates ovarian function. Another example is Ptgs2os, which was identified as a co-expression pair together with protein-coding gene Ptgs2 (Fig. S1). Interestingly, Ptgs2os was located at the opposite strand of promoter region of Ptgs2, indicating a cis-regulatory role of Ptgs2os on Ptgs2 expression (Fig. 5a). It has been found that Ptgs2 deficient female mice had severe developmental disorders, including a series of reproductive dysfunction such as inferitily (Lim et al. 1997). Moreover, PTGS2, the human homolog of Ptgs2, was demonstrated to be a POI candidate gene, and was highly concerned in the clinical diagnosis of POI (Tucker et al. 2016). From this study, our next steps would be to verify the regulatory relationship between lncRNA and mRNA through experiments at the cellular level, clarify the causal relationship, and explain its regulatory mechanism, which would help in further elucidating the etiology of female reproductive diseases.

We have previously revealed 12 protein-coding gene clusters in mouse ovaries throughout the reproductive lifespan (Zhou et al. 2021). Co-expressed gene clusters could be used to further annotate the lncRNA modules. We found that ME2 was highly correlated with Cluster5 and Cluster6 (Fig. S5), which showed overrepresentation in embryonic organ morphogenesis. ME4 is consistent with Cluster3, experiencing transcriptional activation from NN to PB, and henceforth fixing their expression at a stable level. GO terms enriched for cluster 3 are mainly about steroid metabolic process and hormone metabolic process, which suggest that these lncRNAs may also help ovary to get prepared for the puberty stage. Importantly, ME5 is similar to Cluster8 and Cluster9, which were enriched with POI causative genes. It is also possible that lncRNAs in ME5 may regulate female ovarian function through regulating these protein-coding genes, and thus further experiments are required to verify these findings.

To our knowledge, this is the first report of a comprehensive chronological lncRNA expression analysis of mouse ovaries throughout the female reproductive lifespan. However, we have realized that some lncRNA-mediated ovarian development and ageing found in mice might not be analogous to human ovarian development and ageing because the conservation of lncRNA is not as strong as that of protein-coding genes. Moreover, neither can we simply judge whether lncRNAs are functionally conserved by sequence conservation, since lncRNAs with conserved sequences and positions may not have the same function in different species(Guo et al. 2020). It is better to combine the sequence, structure, function and expression of the lncRNA loci to to get an overall understanding. Our next step is to establish a multi-faceted system to evaluate the functional conservation of lncRNAs between mice and humans. It is hoped that the data in model organisms can contribute to understanding human ovarian development and ageing, and further improve the clinical diagnosis of human ovarian ageing-related diseases.

Funding

This work was supported by National Key Research and Development Program of China (2022YFC2703800), National Natural Science Foundation of China (32270658 and 32288101) and Natural Science Foundation of Shanghai (20ZR1407000).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Authors’ contributors

YW, FZ designed and supervised the study. SC, ZZ, XY and YP conducted experiments. SC, ZZ, RL and JM analyzed data. SC, ZZ, YW and FZ wrote the manuscript.

Ethics approval

This work was accomplished with the approval of the Ethics Committee at School of Life Sciences, Fudan University.

Consent to participate

Not applicable. No human participant.

Consent to publish

Not applicable. No human participant.

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Editorial decision: Major Revisions Needed
02 Dec, 2024
Reviewers agreed at journal
21 Oct, 2024
Reviewers invited by journal
18 Aug, 2023
Editor assigned by journal
02 Aug, 2023
First submitted to journal
01 Aug, 2023

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Identification of lncRNA expression profiles associated with ovarian development and ageing process in mice

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Version 1

Abstract

Figures

Introduction

Materials and methods

RNA sequencing and data processing

Identification of differentially expressed genes

LncRNA regulatory targets prediction

Gene ontology (GO) enrichment analysis

Co-expression network

Weighted correlation network analysis (WGCNA)

Cluster-cluster correlation between lncRNA modules and mRNA clusters

Statistical analysis

Results

Identification of lncRNAs in mouse ovaries

Functional annotation of the differentially expressed lncRNAs in mouse ovarian development and ageing

Identification of conserved lncRNA regulatory pairs between mouse and human

Co-expressed modules of lncRNAs in mouse ovarian development and ageing

Discussion

Declarations

References

Supplementary Files

Status:

Version 1