Integrated multiple transcriptomes in oviductal tissue across the porcine estrous cycle reveal functional roles in oocyte maturation and transport

Abstract

Understanding the changes in the swine female reproductive system is important for solving issues related to reproductive failure and litter size. Elucidating the regulatory mechanisms of the natural estrous cycle in the oviduct under non-fertilisation conditions can improve our understanding of its role in the reproductive system. Herein, whole transcriptome RNA sequencing of oviduct tissue samples was performed. The differentially expressed genes (DEGs) were identified for each time point relative to day 0 and classified into three clusters based on their expression patterns. Clusters 1 and 2 included genes involved in the physiological changes through the estrous cycle. Cluster 1 genes were mainly involved in PI3K-Akt signaling and steroid hormone biosynthesis pathways. Cluster 2 genes were involved in extracellular matrix-receptor interactions and protein digestion pathways. In Cluster 3, the DEGs were downregulated in the luteal phase; they were strongly associated with cell cycle, calcium signaling, and oocyte meiosis. The gene expression in the oviduct during the estrous cycle influenced oocyte transport and fertilization. Our findings provide a basis for successfully breeding pigs and elucidating the mechanisms underlying the changes in the pig oviduct during the estrous cycle.

Lay Summary

Understanding the swine female reproductive system is important for solving issues related to reproductive failure and litter size. The oviduct is the site of fertilization. After fertilization, the fertilized egg moves to the uterus for implantation. Elucidating the regulatory mechanisms of the estrous cycle in the oviduct can improve our understanding of their roles. In this study, whole transcriptome RNA sequencing of oviduct tissue samples was performed throughout the estrous cycle to screen for differentially expressed genes (DEGs). The DEGs were classified into three clusters based on their expression patterns. Clusters 1 and 2 included genes involved in the physiological changes observed through the estrous cycle. The expression levels of Cluster 3 genes were downregulated specifically in the luteal phase; this was associated with calcium signalling and oocyte meiosis. In this study, we identified that the expression of genes in the oviduct influences oocyte transport and fertilization, which are the key functions of the oviduct. This study provides a basis for successful breeding in the pig industry and elucidating the mechanisms underlying the changes in the pig oviduct during the estrous cycle.

Integrated time serial transcriptomes and functional analyses of the porcine oviductal tissue throughout the estrous cycle reveal mechanisms underlying oocyte transportation and maturation.

Introduction

Reproductive traits of pigs, including litter size, litters per sow per year, and size of the piglets, are important factors for establishing breeding goals (Irvin and Swiger, 1984). Therefore, a comprehensive understanding of the molecular pathways occurring in the reproductive tissues during the estrous cycle is important for solving reproductive issues, such as infertility or stillbirths, and for consequently controlling the total production per sow. The changes in the levels of steroid hormones during the estrous cycle induce rapid physiological and morphological changes in the female reproductive organs (Akison and Robker, 2012).

The swine estrous cycle lasts about 21 d. As the follicle grows, it produces estrogen, which reaches its peak level shortly before ovulation. In addition, the levels of luteinising hormone (LH) and gonadotropin-releasing hormone are increased, because they are involved in a positive feedback loop with estrogen. However, production of follicle stimulating hormone is inhibited by estrogen, preventing the maturation of other follicles (Henricks et al., 1972). Peaking LH level promotes ovulation and facilitates the release of progesterone from the corpus luteum (CL). Fertilization occurs when an oocyte encounters sperm at the ampullary-isthmic junction. During pregnancy, progesterone secretion continues; in addition, the uterus secretes prostaglandin F2α, which inhibits progesterone secretion from CL (Soede et al., 2011). The female pig organs involved in reproduction include the ovary, oviduct, uterus, and endometrium; the oviduct, especially the ampulla region, which is the site of fertilisation, is crucial as a hormone-sensitive bridge (Younis et al., 1989).

To understand the genetic landscape of the oviduct, most studies have used microarray and candidate genes for analyzing gene expression patterns and variations (Bauersachs et al., 2004; Acuna et al., 2017; Stefańska et al., 2018). However, a recently developed, high-throughput, data-based technology—next-generation sequencing—enables quantitative analysis of dynamic transcriptomes (Wang et al., 2009; Zhao et al., 2014; Park and Kim, 2016; Kim and Kim, 2021). To date, several studies have conducted whole transcriptome analyses to elucidate the molecular mechanisms in reproductive tissues. Based on the correlation between integrated transcriptomes, Kim et al. (Kim et al., 2018) reported that tissue synchronisation occurs among three important reproductive organs (the ovary, oviduct, and endometrium). Fischer et al. (Fischer et al., 2015) identified nonsynonymous mutations in the genes overexpressed in the testis and oviduct, providing a list of candidate biomarkers for reproductive traits in swine. However, the dynamic changes in oviduct transcriptomes during the estrous cycle are not entirely understood. Therefore, the aim of this study was to determine the time-serial transcriptome profile and the molecular mechanisms occurring in the porcine oviduct during the estrous cycle using RNA sequencing (RNA-Seq). We explored the differentially expressed genes (DEGs) at each time point and integrated transcriptome profiles to identify gene expression patterns and their modulations.

Materials and Methods

Animals and experimental design

Twenty-two crossbred (Landrace × Yorkshire) gilts aged 6–8 mo, weighing 100–120 kg, and having had at least two estrous cycles of normal duration (18–22 d) were used in this study. The gilts’ estrous behavior was observed daily in the presence of a boar according to a previously described method (Geisert et al., 1982). The first day of estrous behavior was designated as day 0. Oviduct tissues were collected from the ampulla region on days 0, 3, 6, 9, 12, 15, and 18 (Figure 1A). The collected oviducts were snap-frozen in liquid nitrogen and stored at −80 °C until RNA extraction.

Figure 1.

Oviduct transcriptome analysis during the swine estrous cycle. (A) Oviduct samples at different time points (Day 0; D00, Day 3; D03, Day 6; D06, Day 9; D09, Day 12; D12, Day 15; D15, and Day 18; D18) during the estrous cycle. (B) Relationship between different time point samples using MDS analysis. Each point represents an oviduct sample. (C) Dynamic view of a volcano plot of differentially expressed genes (DEGs) (false discovery rate < 0.05 and log₂ FC ≥ 1 for upregulated and log₂ FC ≤ −1 for downregulated expression) at Days 3, 6, 9, 12, 15, and 18 relative to the expression at Day 0. DEGs with upregulated expression are show in red, and those with downregulated expression are shown in blue. The X and Y axes were scaled to log₂ FC and -log10 P-value. The bar graph shows the number of DEGs at each time point.

All animal-related experimental procedures were performed in accordance with the Guide for Care and Use of Animals in Research, approved by the Institutional Animal Care and Use Committee of the National Institute of Animal Science (No. 2015-137).

RNA isolation, library construction, and sequencing

Total RNA was isolated from the oviduct tissue using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocols (Simms et al., 1993). The isolated RNA was quantified using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). One microgram mRNA was used to prepare a library using the Illumina TruSeq RNA Sample Preparation Kit. Paired-end (2 × 100 bp) sequencing was performed on an Illumina HiSeq 2000 platform (Illumina, Inc., San Diego, CA). Briefly, the mRNA was fragmented, the fragments were copied into first-strand cDNA using reverse transcriptase and random primers, and into second-strand cDNA using DNA Polymerase I and RNase H. Finally, the cDNA was subjected to end repair process.

Data processing and DEG identification

Total paired-end sequence reads were generated from 22 samples. Prior to RNA-Seq analysis, the quality of the reads was checked using FastQC v0.11.4 (Andrews, 2010). The reads were trimmed, and the adapters were removed using SLIDINGWINDOW:4:15 and MINLEN 75 in Trimmomatic-0.38 (Williams et al., 2016). The trimmed reads were mapped to the reference genome (Sus_scrofa.Sscrofa11.1.98) using hisat2 v2.1.0 (Kim et al., 2015). Subsequently, SAMtools v1.9 was used to sort mapped reads and convert the data into a binary file format (Li et al., 2009). The sorted binary alignment/map files were quantified using featureCounts (subread-1.6.3-Linux-x86_64) (Liao et al., 2014). Trimmed mean of M-values (TMM) normalization (Robinson and Oshlack, 2010) was applied to the raw counts to estimate the relative RNA levels using R package edgeR (Robinson et al., 2010). DEGs were identified for each time point (days 3, 6, 9, 12, 15, and 18), relative to day 0, using thresholds of absolute log₂ fold change (FC) ≥ 1 and a false discovery rate < 0.05. To confirm similarities among the samples corresponding to each time point, MDS was performed using the R package limma (Ritchie et al., 2015).

Functional annotation and clustering analysis

Total DEGs were annotated using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. DAVID v6.8 was used to identify enriched molecular mechanisms. GO was applied to classify gene products into three independent categories: biological processes, molecular functions, and cellular components. Enriched GO terms were visualized as treemaps using REVIGO (Supek et al., 2011), and enriched KEGG pathways were illustrated as bar plots with fold enrichment and P-values.

DEGs with similar expression patterns, which showed significant expression during at least one of the investigated time points, were clustered using the k-means method with the Pearson correlation distance metric and 1K iterations using Multi-Experiment Viewer (MeV) v4.9.0 (Howe et al., 2011). Genes included in each cluster were annotated using the KEGG database, and a network was constructed using ClueGO v2.5.5 with P-values < 0.05 and default values for other options (minimum number of genes = 3, minimum percentage of genes per term = 4%, and kappa score = 0.4; Bindea et al., 2009).

Gene set enrichment analysis (GSEA)

GSEA v4.0.3 based on KEGG was performed to understand the differences in expression of ranked genes at day 12 relative to day 0 (Subramanian et al., 2005). In GSEA, TMM normalized counts corresponding to all samples at days 0 and 12 were used. A normalized enrichment score was calculated using weighted Kolmogorov–Smirnov statistical method based on ranked FCs. To calculate the significance of each KEGG term, a permutation test was performed. The core enriched genes were visualized in a heatmap, and the gene expression modulation for each selected KEGG pathway was illustrated using R package clusterProfiler (Yu et al., 2012).

Results

DEG profiling from the RNA-Seq data

The RNA-Seq data were generated with an average of 18,978,423 total reads from day 0 samples, while samples from days 3, 6, 9, 12, 15, and 18 had averages of 19,492,946; 19,275,046; 18,949,884; 19,281,551; 20,340,490; and 19,497,231 total reads, respectively. Unique mapping rates averaged 94.81%, and overall mapping rates averaged 98.24% (Table 1). After mapping the reads on the porcine reference genome, similarities among samples were confirmed based on a multidimensional scaling (MDS) plot, and each time point was clearly distinguished (Figure 1B). The similarity in gene expression was higher on days 6, 9, and 12, which correspond to the luteal phase, compared to that on days 0, 3, 15, and 18, which correspond to the follicular phase.

Table 1.

Summary of RNA sequencing results and read alignment

No	Treatment	ID	Raw Data			After Trimmomatic			Trimming rate	Mapping data
			Read	Sequence length	%GC	Read	Sequence length	%GC	(%)	Uniquely mapped read (%)	Overall alignment rate (%)
1	Control	D0C-Ovid-1	17,961,314	101	51	15,342,621	75–101	50	14.57962931	94.85	98.29
2		D0C-Ovid-2	19,106,487	101	51	16,281,159	75–101	50	14.78727094	94.82	98.33
3		D0C-Ovid-3	19,867,469	101	51	17,014,555	75–101	50	14.35972544	94.86	98.37
4	Treat	D3C-Ovid-1	19,772,368	101	51	16,953,808	75–101	50	14.25504522	94.94	98.34
5		D3C-Ovid-2	19,530,742	101	51	16,828,423	75–101	50	13.83623315	94.92	98.39
6		D3C-Ovid-3	19,175,729	101	50	16,371,028	75–101	49	14.62630704	94.40	98.05
7	Treat	D6C-Ovid-2	18,510,823	101	50	15,731,900	75–101	49	15.01242273	94.97	98.21
8		D6C-Ovid-3	20,039,268	101	50	17,036,473	75–101	49	14.98455433	95.04	98.21
9	Treat	D9C-Ovid-1	18,665,067	101	50	16,000,684	75–101	49	14.27470365	94.82	98.19
10		D9C-Ovid-2	18,732,721	101	50	16,011,408	75–101	49	14.52705669	94.70	98.11
11		D9C-Ovid-3	19,451,864	101	51	16,599,341	75–101	50	14.66452264	94.63	98.20
12	Treat	D12C-Ovid-1	19,885,880	101	50	17,038,736	75–101	50	14.31741517	94.81	98.25
13		D12C-Ovid-2	17,698,398	101	50	15,111,105	75–101	49	14.6187977	94.79	98.19
14		D12C-Ovid-3	18,994,386	101	50	16,222,068	75–101	49	14.59545994	94.87	98.20
15		D12C-Ovid-4	20,547,540	101	50	17,823,244	75–101	49	13.25850199	94.76	98.12
16	Treat	D15C-Ovid-1	21,071,137	101	51	18,332,506	75–101	50	12.99707273	94.35	98.13
17		D15C-Ovid-2	21,085,713	101	50	18,321,567	75–101	49	13.1090943	94.70	98.10
18		D15C-Ovid-3	20,769,207	101	51	17,992,010	75–101	50	13.37170456	94.84	98.20
19		D15C-Ovid-4	18,435,904	101	51	15,916,002	75–101	50	13.66844826	94.67	98.23
20	Treat	D18C-Ovid-1	20,404,927	101	50	17,614,747	75–101	50	13.67405039	94.78	98.20
21		D18C-Ovid-2	19,555,704	101	50	16,979,038	75–101	49	13.17603294	95.33	98.58
22		D18C-Ovid-3	18,531,061	101	50	16,045,886	75–101	50	13.4108619	94.96	98.35

The number of DEGs gradually increased n days 3 (n = 641), 6 (n = 1,450), and 9 (n = 1,879). The number of DEGs peaked on day 12 (n = 2,343) and then decreased steadily from day 15 (n = 932) to day 18 (n = 423). The numbers of DEGs with upregulated and downregulated expression compared to day 0 showed the same trend as the total number of DEGs. Moreover, the number of DEGs (n = 4,931) with downregulated expression was consistently greater than that of DEGs with upregulated expression (n = 2,692) (Figure 1C and Supplementary Table S1).

Functional annotations

GO enrichment analyses were performed to identify the biological processes of DEGs at each time point, and the results are displayed in treemaps indicating -log₁₀ P-value as an area (Figure 2). GO terms were enriched in the negative regulation of osteoblast differentiation on day 3 and significantly enriched in the positive regulation of cell proliferation on days 6, 9, and 12. On day 15, collagen fibril organization was enriched, while on day 18, the chemokine-mediated signaling pathway was enriched.

Figure 2.

Gene Ontology treemaps of biological process terms according to time points using differentially expressed genes from databases in DAVID. Treemaps were constructed based on P-values (A–F).

At each time point, enriched pathways for DEGs were determined using the KEGG database (Figure 3 and Supplementary Table S2). Cell cycle, adrenergic signaling in cardiomyocytes, and p53 signaling pathway were enriched on day 3. On day 6, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling pathway were enriched. Calcium signaling pathway, neuroactive ligand-receptor interaction, and proteoglycans in cancer were enriched on days 9 and 12. Genes sampled on day 15 were associated with enrichment in neuroactive ligand-receptor interaction, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. On day 18, cell cycle, DNA replication, and HTLV-I infection were significantly enriched.

Figure 3.

Enrichment analysis of differentially expressed genes in KEGG pathways from databases in DAVID. Enriched pathways for the time points were applied in the order of fold change and −log₁₀ P-value. Each graph shows the results in the order of progressing estrous cycle. (A; Day 3, B; Day 6, C; Day 9, D; Day 12, E; Day 15, F; Day 18).

Notably, analyses of gene expression on days 6, 9, and 12 (the luteal phase of the estrous cycle) revealed five common, significantly enriched pathways (calcium signaling pathway, ECM-receptor interaction, focal adhesion, oocyte meiosis, and PI3K-Akt signaling pathway), and these days were closely grouped in the MDS plot (Figure 1B) and had highly increased numbers of DEGs (Figure 1C).

Clustering by gene expression pattern

To examine the expression patterns of DEGs at different time points of the estrous cycle, the entire set of identified DEGs was adopted for k-means clustering analysis and divided into three major clusters. Next, we applied network analysis based on the KEGG database using the ClueGO plugin to determine the functions of the clustered genes. Clusters 1 and 2 consisted of 1,222 and 1,146 genes, respectively, which showed contrasting expression patterns. The expression of genes in Cluster 1 was upregulated throughout the estrous cycle, while the expression of genes in Cluster 2 was downregulated (Figure 4A and B). A network was constructed with genes from Clusters 1 and 2 involved in any of the five significant KEGG pathways (Figure 4C). The genes in Cluster 1 were primarily involved in the PI3K-Akt signaling and steroid hormone biosynthesis pathways, while the genes in Cluster 2 were involved in the ECM-receptor interaction and protein digestion and absorption pathways. Figure 5 shows that the Cluster 3 showed a clearly different pattern of gene expression compared to Clusters 1 and 2. The pattern in Cluster 3 showed uniquely downregulated genes on day 12 (Figure 5A). Subsequently, we constructed a network from 1,000 genes of Cluster 3 associated with six significant KEGG pathway terms (cell cycle, compartment and coagulation cascades, calcium signaling, insulin secretion, oocyte meiosis, and dilated cardiomyopathy; Figure 5B). Among these pathways, those involved in calcium signaling and oocyte meiosis were not only validated for their high activity on day 12 referring to the KEGG pathways (Figure 5B), but were significantly represented in the GO and KEGG results of the luteal phase, which validated their significance (Figures 1 and 2).

Figure 4.

Differentially expressed genes (DEGs) sorted using Multi-Experiment Viewer Clustering. (A) Cluster 1 contained 1,222 DEGs (gene expression pattern in red). (B) Cluster 2 contained 1,146 DEGs (gene expression pattern in blue). Overall gene expression patterns for Clusters 1 and 2 was similar, but Cluster 1 showed an upregulated trend, whereas Cluster 2 showed a downregulated trend. (C) ClueGO (v. 2.5.5; P-value ≤ 0.05, kappa score = 0.4) plugin was used with the KEGG database to visualise genes related to each pathway.

Figure 5.

Differentially expressed genes (DEGs) sorted using Multi-Experiment Viewer Clustering. (A) Cluster 3 contained 1,000 DEGs (gene expression pattern in green) and showed a downregulated expression trend on Day 12. (B) ClueGO (v. 2.5.5, P-value ≤ 0.05, kappa score = 0.4) plugin was used with the KEGG database to visualize genes related to each pathway.

Gene set enrichment analysis

Further analysis was focused on the transcriptomic changes on day 12 in Cluster 3, which showed a uniquely and significantly downregulated expression profile during the estrous cycle. In the GSEA, calcium signaling pathway and oocyte meiosis were highly ranked terms (Figure 6A). The heatmaps showed 61 core enriched genes in the calcium signaling pathway and 37 in oocyte meiosis (Figure 6B and C). The modulation in the expression of genes related to the two pathways indicated that they were mostly downregulated (Figure 6D and E).

Figure 6.

Gene set enrichment analysis (GSEA) for comparisons of gene expression between Days 0 and 12. Scattered bubble plot from GSEA based on the KEGG database. The normalized enrichment score (NES) is represented on the X-axis, and the bubble color indicates the -log₁₀ P-value. (A) The size of the bubble represents the number of genes counted in the terms. (B and C) A heatmap of the downregulated genes on Day 12 in the enriched pathways of oocyte meiosis and calcium signaling. (D and E) Changes in expression of differentially expressed gene in the oocyte meiosis and calcium signaling pathways on Day 12.

Discussion

DEGs at different time points of the estrous cycle in the oviduct during the luteal phase

During the estrous cycle, ovarian steroid hormones, including oestrogen and progesterone, affect oviductal, morphological, and functional responses (Akison and Robker, 2012). Therefore, it is important to understand the changes in molecular mechanisms and functions of the oviduct during the estrous cycle. The hypothesis is that, analyzing the changes in gene expression in the porcine oviduct during the course of the estrous cycle will enable identifying the differences in the biological function of the oviducts during the follicular and luteal phases. In this study, the molecular mechanisms regulating dynamic changes in the transcriptome at different time points of the estrous cycle were identified. As shown in the MDS plot, during the luteal phase (days 6, 9, and 12), samples were grouped together by transcriptome expression on each day and showed similar expression patterns (Figure 1B). The numbers of DEGs on days 6, 9, and 12 were 1,405; 1,879; and 2,343, respectively, indicating that the number of significant DEGs gradually increased during the luteal phase. After ovulation, the oocyte is transported through the oviduct and reproductive system. This could influence the expression of genes that function in cell growth and development, to prepare for pregnancy and to maintain the pregnancy (Kim et al., 2018). Among KEGG pathways enriched in the luteal phase (Figure 3), the calcium signaling pathway plays an important role in normal oocyte development in the oviduct (Stricker, 1999), while ECM-receptor interaction plays a role in protecting and supporting cells during ovulation (Russell and Salustri, 2006; Bazer et al., 2012). Focal adhesions are involved in receptor binding to the ECM, which is crucial for oocyte fertilization (Kim et al., 2018). Oocyte meiosis is regulated by progesterone, which promotes oocyte development until fertilization. The PI3K-Akt signaling pathway regulates cell processes such as metabolism, proliferation, and cell survival (Andrade et al., 2017).

Cell-to-cell interaction and proliferation in the oviduct

Using k-means clustering, we selected three clusters with similar gene expression patterns based on the KEGG pathway database (Figure 3). The overall physiological changes through the course of estrous cycle were identified by integrating and analyzing Clusters 1 and 2 (Figure 4A and B). In particular, the significantly enriched pathways, including ECM-receptor interaction, PI3K-Akt signaling, protein digestion and absorption, glycosaminoglycan biosynthesis, and proteoglycans in cancer, were remarkably associated with oviductal activities during the estrous cycle (Figure 4C). The PI3K-Akt signaling pathway is related to cell physiology and proliferation (Dobbin and Landen, 2013) and stimulates follicle development and oocyte growth (Andrade et al., 2017). The ECM plays an essential role in embryo development and regulates different cellular processes via expression of ECM proteins (Juliano and Haskill, 1993; Roskelley et al., 1995). Moreover, interactions between the cumulus-oocyte complex and epithelial cells of the oviduct are regulated at the level of the ECM (Ulbrich et al., 2004). Through its complexes of elastin, proteoglycans, and glycosaminoglycans, the ECM induces uterus dilation through collagen fiber degradation during the estrous cycle (Rodriguez-Pinon et al., 2015). Our results revealed significant roles of time-dependent transcriptomic changes in cell growth, proliferation, and cell-cell interaction during the estrous cycle.

Oocyte meiosis and calcium signaling pathway

The expression levels of Cluster 3 genes gradually increased over the luteal phase, but rapidly decreased after day 12 (Figure 5A). The oocyte meiosis and calcium signaling pathway were most significantly enriched in Cluster 3 genes on day 12 (Figure 5B). Similar results appeared in the top terms when listed in order of significance based on the pathways activated on day 12 and identified via GSEA (Figure 6A).

Oocyte meiosis is related to the formation and development of oocytes and is an important process involved in oocyte migration and fertilization, the core functions of the oviduct (Luvoni et al., 2003). IGF1, PGR, ITPR3, CAMK2A, CAMK2B, IP3R, BUB1, and CDC20 expression was downregulated in the enriched KEGG pathways (Figure 6B and D). IGF1, PGR, ITPR3, CAMK2B, and PLK1 are involved in oocyte-related processes. IGF1 plays an important role during the early estrous cycle, and ovulation fails if IGF1 is not expressed (Velazquez et al., 2009). In vivo, IGF1 may indirectly induce the stimulation of cumulus and/or granulosa cells, resulting in improved oocyte maturation and fertilization (Xia et al., 1994). PGR encodes a progesterone receptor that mediates the effects of progesterone in the oviduct. It plays an important role in the successful release of oocytes from preovulatory follicles (Kim et al., 2009) as well as in ciliary beat frequency and oocyte transport (Akison and Robker, 2012). ITPR3 is involved in the initiation and propagation of intracellular Ca²⁺ signaling during the follicular phase (Mikoshiba, 2007), while CAMK2B and PLK1 encode essential factors involved in Ca²⁺-induced exit from mitosis (Hansen et al., 2006). Therefore, these genes are involved in successful ovulation and fertilisation. Based on the predominance of downregulated genes on day 12, estrous cycle-related activities take place at a site other than the oviduct, and that active activities occur in the follicular phase where oocytes are present in the oviduct.

KEGG pathway analysis identified calcium signaling as the most enriched pathway. Progesterone, an ovarian steroid hormone secreted in the oviduct, activates calcium channels and increases intracellular calcium levels in the sperm to promote their hyperactivity (Coy et al., 2012). Sperm hyperactivity assists in escaping epithelial folds, allowing the sperm to reach the oocyte (Demott and Suarez, 1992). Among the significant DEGs identified by GSEA, ITPR1 and CAMK1D are involved in calcium signalling pathways and associated with fertilisation (Figure 6C and E), ITPR1 modulates intracellular calcium ion concentrations during mammalian fertilization (Malcuit et al., 2005); CAMK1D also serves as a mediator of calcium-dependent cellular processes (Braun and Schulman, 1995). We speculate that the predominance of downregulated genes during the luteal phase (day 12) may indicate that their roles are less relevant to fertilization.

Oocyte transport is an essential function of the oviduct. In the oviduct, oestrogen and progesterone regulate oocyte transport, cilia beating, and smooth muscle contraction (Mizobe et al., 2010). Cluster profiling revealed a gene involved in the calcium signalling pathway that plays a role in smooth muscle contraction in the oviduct (Figure 6E). DEGs related to oocyte transport were identified using functional analysis and GSEA. Activation of CHRM3 is associated with muscle contraction because the M₃ receptor regulates intracellular calcium levels (Yang et al., 1993). TNNC1 is involved in calcium contractile events by binding with Ca²⁺ (Herzberg et al., 1986). Downregulation of these genes on Day 12 and observation of smooth muscle relaxation explains the inhibition of muscle contraction in the luteal phase.

Conclusion

This study provides novel insights into the dynamic changes in the molecular mechanisms in the oviducts of non-fertilization gilts during the estrous cycle, using RNA-Seq technology. In the oviduct, genes associated with oocyte meiosis and calcium signaling pathways are significantly enriched during the estrous cycle, because their expression influences oocyte transport and fertilization, the core functions of the oviduct. These results strongly suggest that oocyte maturation and smooth muscle contraction are regulated by IGF1, PGR, ITPR1, and CHRM3. This study could be a valuable resource for further studies on porcine reproductive tissue and may serve as a basis for successful reproduction and breeding in the pig industry, because it elucidates the mechanisms underlying the changes in the pig oviduct during the estrous cycle. We expect future studies to further clarify the regulatory roles of genes that are differentially expressed in the follicular and luteal phases.

Glossary

Abbreviations

CL

corpus luteum

DEGs

differentially expressed genes

ECM

extracellular matrix

FC

fold change

FDR

false discovery rate

GO

gene ontology

GSEA

Gene Set Enrichment Analysis

KEGG

Kyoto Encyclopedia of Genes and Genomes

LH

luteinizing hormone

MDS

multidimensional scaling

MeV

Multi-Experiment Viewer

RNA-Seq

RNA sequencing

TMM

trimmed mean of M-values

Conflict of interest statement

The authors declare no real or perceived conflicts of interest.