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Article

Transcriptomic Profiling Reveals Urokinase-Type Plasminogen Activator-Mediated Regulation of Metabolic Competence and Cumulus Expansion During Mouse Oocyte In Vitro Maturation

by
Ling-Yu Yeh
1,
Christine Shan-Chi Chiu
1,2,3,
Kuan-Sheng Lee
2,4,
Robert Kuo-Kuang Lee
1,2,
Ming-Huei Lin
1,2,4,5,
Yuh-Ming Hwu
6 and
Sheng-Hsiang Li
1,7,*
1
Department of Medical Research, MacKay Memorial Hospital, New Taipei 251, Taiwan
2
Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei 104, Taiwan
3
Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei 100, Taiwan
4
Department of Medicine, MacKay Medical University, New Taipei 252, Taiwan
5
MacKay Junior College of Medicine, Nursing, and Management, Taipei 112, Taiwan
6
Taipei Fertility Centre, Taipei 110, Taiwan
7
Department of Chemical Engineering & Biotechnology, National Taipei University of Technology, Taipei 106, Taiwan
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2026, 27(4), 1781; https://doi.org/10.3390/ijms27041781
Submission received: 31 December 2025 / Revised: 6 February 2026 / Accepted: 7 February 2026 / Published: 12 February 2026
(This article belongs to the Special Issue New Advances in Germ Cell Research)
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Abstract

In vitro maturation (IVM) of mammalian oocytes is an essential fertility option for patients at risk of ovarian hyperstimulation syndrome or needing urgent fertility preservation. However, poor outcomes indicate a limited understanding of the molecular mechanisms behind cumulus cell expansion and extracellular matrix (ECM) remodeling. This study investigated the role of serum-derived urokinase-type plasminogen activator (PLAU) in mouse oocyte IVM. Immature cumulus–oocyte complexes from CD-1 mice were cultured with or without serum, and PLAU activity was blocked using 4-chlorophenylguanidine hydrochloride. Cumulus expansion, oocyte maturation, and cumulus cell transcriptomes were analyzed. Serum supplementation enhanced cumulus expansion and maturation, while absence of serum or PLAU inhibition hindered both processes. External PLAU partially rescued these issues under serum-free conditions. Transcriptome analysis demonstrated that inhibiting PLAU activity reduces the expression of ovulation- and metabolism-related genes, such as those involved in glycolysis and carbohydrate metabolism, while increasing genes related to vesicle-mediated transport. PLAU is crucial for cumulus expansion and metabolic regulation during IVM, affecting ECM remodeling and oocyte quality. Supporting IVM culture media with proteolytic and metabolic factors could improve outcomes in assisted reproduction.

Graphical Abstract

1. Introduction

In vitro maturation (IVM) of human oocytes is a practical option for patients at risk of ovarian hyperstimulation syndrome, those needing urgent fertility preservation, and individuals unable to undergo controlled ovarian stimulation. The American Society for Reproductive Medicine recently recognized IVM’s clinical efficacy in specific applications, classifying it as non-experimental [1]. Oocyte maturation, however, involves complex endocrine signaling, oocyte–follicle interactions, and intracellular kinase and phosphatase activity [2]. Despite its potential, the clinical success rate of IVM remains suboptimal, requiring further research into oocyte maturation mechanisms to improve developmental competence and embryo quality [3,4].
During maturation, oocytes acquire developmental competence through interaction with surrounding granulosa cells [5]. Cumulus cells, a specialized subset of granulosa cells, form a structural and functional complex around the oocyte. The preovulatory surge of luteinizing hormone (LH) activates mural granulosa cells, stimulating cumulus cells to produce a substantial extracellular matrix (ECM) that supports the expansion of the cumulus–oocyte complex (COC) [6]. Complete COC expansion is essential for proper oocyte maturation and subsequent fertilization [7,8]. Studies have shown that mammalian oocytes without cumulus cell layers experience metabolic dysregulation during IVM, along with decreased fertilization capacity and reduced potential for later embryonic development [9,10]. Cumulus expansion, a tissue remodeling process, relies on ECM proteolysis, highlighting the critical role of proteases in initiating this process [11].
Plasminogen, a zymogen in the ECM, is converted into the active protease plasmin by plasminogen activators (PAs) [12,13,14]. In murine cumulus cells, the primary PA is urokinase-type plasminogen activator (PLAU). Interaction of PLAU with its glycosylphosphatidylinositol-anchored receptor (PLAUR) triggers activation of plasmin and matrix metalloproteinases, which are crucial for ECM breakdown and tissue remodeling [15,16]. In addition to its role in proteolysis, PLAUR also acts as a signaling receptor that affects cellular functions, including proliferation, migration, invasion, and adhesion [17,18]. These roles are essential for maintaining tissue homeostasis and supporting repair processes.
PLAU is involved in oocyte maturation through its role in ECM remodeling. In porcine oviducts, PLAU expression increases significantly after ovulation [19]. In mice, gonadotropin stimulation rapidly induces PLAU production in granulosa cells. Cumulus cells isolated from COCs express PLAU in response to in vitro treatment with follicle-stimulating hormones (FSHs) [20,21]. Additionally, PLAU mRNA has been detected in cumulus cells of both immature and in vitro-matured COCs, and immunolocalization studies using an anti-PLAUR antibody have shown PLAUR on the oocyte plasma membrane and within cumulus cells of bovine immature COCs [22,23]. These findings collectively suggest that PLAU may help regulate ECM dynamics in the follicular environment to support oocyte maturation.
Serum components have been shown to enhance oocyte maturation across various species. In canine oocytes, the inclusion of serum affects meiotic spindle formation [24]. In contrast, using estrous cow serum or fetal bovine serum in bovine oocytes yields significantly higher maturation rates than serum-free media [25]. For porcine oocytes, serum supplies low-molecular-weight, heat-stable factors that promote cumulus expansion [26]. Moreover, human umbilical cord serum has been considered a significant component of the IVM medium, as it has been shown to significantly enhance oocyte maturation rates, improve embryo quality, and increase clinical pregnancy outcomes compared with other supplements [27]. Since PLAU is a common serum component [28], it may assist oocyte maturation through its role in ECM remodeling and cumulus expansion.
Our previous research has shown that the PLAU-specific inhibitor amiloride can inhibit the expansion of mouse cumulus cells and oocyte maturation [29]. Roldán-Olarte and co-authors found that, in a bovine model, amiloride-mediated PLAU inhibition reduces embryo yield and alters the expression of genes related to cell adhesion, oxidative stress, the cell cycle, and cysteine protease inhibitors. Their findings suggest that uPA proteolytic activity is necessary for successful oocyte maturation in bovine species [30]. We know that cumulus cell expansion affects subsequent oocyte maturation; thus, we hypothesized that inhibiting PLAU enzyme activity would impair cumulus cell expansion, thereby compromising subsequent oocyte maturation. The primary objective of this study is to elucidate how serum-derived PLAU influences IVM of mouse oocytes, using a PLAU-specific inhibitor, RNA sequencing (RNA-seq), and bioinformatics to uncover the molecular mechanisms by which PLAU affects cumulus expansion during COC maturation.

2. Results

2.1. Dose-Dependent Effects of 4-Cgh on COC Size and Oocyte Maturation During IVM

In our earlier study, we used amiloride to block PLAU enzyme activity. Because amiloride must be dissolved in dimethyl sulfoxide, which can interfere, we chose the water-soluble PLAU inhibitor 4-chlorophenylguanidine hydrochloride (4-Cgh). This compound demonstrated dose-dependent inhibition of PLAU activity (see Figure 1), confirming its effectiveness as an inhibitor of PLAU enzymatic function.
To assess how PLAU inhibition affects cumulus expansion and oocyte maturation during murine IVM, we added increasing concentrations of 4-Cgh to the IVM medium. Compared to the control (Figure 2a), elevating 4-Cgh from 0.25 mM to 0.5 mM and 1 mM resulted in progressive decreases in COC diameter after 16 h of culture (Figure 2b–d), which were statistically significant (Figure 2e). With respect to nuclear maturation, 0.25 and 0.5 mM 4-Cgh had little effect; however, at 1 mM, oocytes predominantly remained at the GV stage, and progression to metaphase II (MII) was blocked entirely (Figure 2f).

2.2. Effects of Serum, 4-Cgh, and PLAU on Cumulus Expansion and Oocyte Maturation During IVM

In the control group, 10% serum in the culture medium resulted in complete cumulus expansion (243 ± 37 µm; light blue squares) and a nearly complete oocyte maturation rate (91.84 ± 7.3%). In contrast, both COC expansion (159 ± 14 µm; light green squares) and oocyte maturation rate (39.13 ± 6.1%) were significantly reduced in serum-free medium. 4-Cgh showed an inhibitory effect. Adding 1 mM 4-Cgh to the serum-containing medium significantly reduced both COC expansion (175 ± 17 µm; dark blue squares) and oocyte maturation rate (0%). Adding 2 U PLAU to the serum-free medium partially restored COC expansion (180 ± 18 µm; dark green squares). In addition, PLAU treatment modestly increased the rate of mature oocytes compared with the serum-free group (65.12 ± 20.5% vs. 39.13 ± 6.1%) (Figure 3).

2.3. Serum Supplementation Counteracts the Inhibitory Effect of 4-Cgh and Restores Cumulus Expansion and Oocyte Maturation in a Dose-Dependent Manner

1 mM 4-Cgh almost completely blocked oocyte maturation, making it difficult to determine whether serum supplementation could alleviate this inhibition. Therefore, we initially tested 0.8 mM 4-Cgh, which significantly hampered both cumulus expansion and oocyte maturation (see Table S1). Using a control medium with 10% serum, complete cumulus expansion (light blue squares, Figure 4a) and oocyte maturation rate (Figure 4b) were measured. Adding 0.8 mM 4-Cgh to this control significantly lowered both parameters. However, as serum concentrations increased, the initial reduction in COC diameter and oocyte maturation caused by 4-Cgh gradually diminished. At a serum concentration of 20%, the inhibitory effect of 0.8 mM 4-Cgh was notably reversed. Although the COC diameter (indicated by the red square) only partially recovered compared with the control group (244 ± 20 µm versus 286 ± 25 µm), the oocyte maturation rate improved to a level that was not significantly different from that of the control group (79.3 ± 12.3% versus 100%).

2.4. Inhibiting PLAU Enzymatic Activity Delays Cumulus Expansion and Alters Gene Expression in Cumulus Cells During IVM

Using time-lapse imaging, we monitored hourly changes in COC diameter (Figure 5a). After 16 h of culture, the average COC diameter in the control group was approximately 300 μm (red line). In contrast, the average diameter in the 1 mM 4-Cgh treatment group was only 230 μm (blue line). Analysis of hourly diameter differences between groups (green line) showed that the divergence became significant at 6 h of culture and then plateaued from 6 to 16 h.
Quantitative analysis of fluorescence images showed no significant difference in cumulus cell viability between the control and 1 mM 4-Cgh groups (Figure 5b).
RNA-seq identified 46,202 genes across all samples. Principal component analysis (PCA) indicated that PC1 and PC2 explained 13.8% and 13.0% of the total variance, respectively. Despite a tendency toward groupwise clustering, overall expression profiles were relatively consistent, likely reflecting the shared cell type (Figure 6a). A volcano plot highlighted differentially expressed genes (DEGs), defined as fold change > 1.5 and p < 0.05 (red; Figure 6b). In total, 1340 DEGs were identified: 486 were higher in controls, and 854 were upregulated in the 4-Cgh group. Their expression patterns are shown in the heatmap (Figure 6c). To validate gene expression, we selected eight genes with relatively high RPKM values and known roles in oocyte maturation and cumulus expansion for quantitative real-time PCR (qPCR) validation; the observed fold-change trends were consistent with the RNA-seq results (Figure 6d).

2.5. Network Analysis Indicates That 4-Cgh Primarily Affects Pathways Related to Cellular Metabolism, the Ovulatory Cycle, and Intracellular Transport

Using STRING (PPI database), we analyzed 255 highly expressed genes (RPKM > 15) and built an interaction network with 246 nodes and 578 edges, which was visualized in Cytoscape (Figure 7). In the visualization, genes upregulated by 4-Cgh are shown in red and downregulated genes in blue; color intensity reflects the fold change magnitude, node size indicates each protein’s importance within the network, and edge darkness represents interaction strength.
GO enrichment performed with STRING’s built-in functions clustered biological processes and, after manual annotation, these were grouped by functional clusters or GO terms for visualization (Figure 8). Significantly enriched processes included small-molecule metabolism, carboxylic-acid metabolism, oxoacid metabolism, canonical glycolysis, ovulatory-cycle process, regulation of the intrinsic apoptotic signaling pathway, organelle disassembly, Golgi-apparatus interactions, and endoplasmic reticulum–Golgi (ER–Golgi) vesicle-mediated transport (Figure 8).
Regarding expression trends, genes associated with canonical glycolysis and the ovulatory cycle were uniformly downregulated. Most genes involved in carbohydrate-derived and oxoacid metabolic processes were also downregulated. In contrast, genes related to ER–Golgi vesicle-mediated transport and the majority of genes involved in protein transport were upregulated. These pathway-level expression patterns are further illustrated in the heatmap (see Figure 9).
The genes associated with the ovulation cycle include not only those that regulate ovulation but also several crucial genes involved in cumulus expansion (Figure 9b). Genes such as Ereg, Ptgs2, and Timp1 play roles in controlling the ovulation process. Additionally, genes such as Ptx3, Vcan, and Adamts1, which are integral to the ovulation cycle and closely linked to the formation and remodeling of the cumulus cell extracellular matrix, were significantly downregulated in the 4-Cgh group. Moreover, other essential genes involved in cumulus cell expansion, including Has2 and Tnfaip6, were consistently downregulated. These findings suggest that 4-Cgh treatment may influence the expression of genes involved in cumulus cell expansion and matrix remodeling during the ovulation cycle.

3. Discussion

Our study demonstrates that 4-Cgh, a water-soluble PLAU inhibitor, significantly reduces COC expansion and oocyte nuclear maturation during IVM, highlighting the essential role of PLAU in these processes. Interestingly, the inhibitory effects of 4-Cgh were partially attenuated by increasing the serum concentration in the culture medium, suggesting that serum-derived PLAU and other extracellular components are vital for supporting oocyte maturation. Through RNA-seq analysis, we also found that inhibiting PLAU enzyme activity reduced the expression of genes involved in ovulation and disrupted multiple metabolic pathways in cumulus cells.
Although purified PLAU alone did not fully restore inhibition of cumulus expansion and oocyte maturation in our system, serum supplementation was beneficial. This may reflect the complexity of PLAU activation in vivo, where circulating PLAU predominantly exists in an inactive form bound to its receptor (PLAUR) and requires plasminogen for proteolytic activation [31]. These findings suggest that functional PLAU signaling in cumulus cells depends on the presence of active PLAU–PLAUR–plasminogen complexes, which may be supplied through serum components.
Cumulus cells play a critical role in supporting oocyte maturation by supplying key metabolites, structural molecules, and paracrine factors. In bovine species, it has been well established that oocytes matured in vitro without surrounding cumulus cells exhibit severe metabolic dysfunction, reduced fertilization capacity, and poor embryonic development [8,31]. Transcriptomic analyses of human oocytes similarly show that the absence of cumulus cells compromises developmental potential and alters gene expression profiles [32,33]. This is mainly because, in mammals, oocytes lack transporters for several essential molecules—including glucose, cholesterol, and certain amino acids—and therefore rely on cumulus cells for metabolic support [34,35]
In particular, cumulus cells convert glucose into pyruvate via glycolysis, which is then transported to the oocyte to support oxidative phosphorylation and meiotic progression [36,37,38,39,40,41]. In our study, 4-Cgh-treated cumulus cells exhibited significant downregulation of key glycolytic enzymes, including Pfkl, Tpi1, Pkm, and Eno1. Additionally, the rate-limiting enzymes HK1/2, which catalyze the first step of glycolysis, were downregulated after 4-Cgh treatment (Figure 9a), indicating impaired glucose metabolism that may disrupt energy supply to the oocyte and impair its maturation.
Furthermore, we observed a downregulation of genes involved in carbohydrate derivative metabolism, including those related to UDP-glycan, aminoglycan, and other glycan precursors. This pathway is vital not only for energy regulation and signal transduction but also for the biosynthesis of glycosaminoglycans, essential components of the extracellular matrix (ECM) [42,43]. Additionally, several crucial genes in the cumulus cell expansion matrix, including Has2, Ptx3, Tnfaip6, and Vcan, were downregulated. Interestingly, although there was no significant difference in Plau and Plaur transcriptional levels, several ECM-remodeling proteolytic enzyme genes, such as Adamts1 and Timp1 [44,45], were significantly affected by 4-Cgh treatment. This indicates that PLAU may influence the metabolic and structural pathways needed for cumulus cell expansion not by changing its own expression, but by regulating downstream proteolytic networks involved in matrix remodeling, thereby affecting key matrix components.
In parallel, the downregulation of genes involved in oxoacid metabolism, including those related to tricarboxylic acid cycle intermediates such as α-ketoglutarate and pyruvate, indicates reduced mitochondrial function and impaired oxidative metabolism in cumulus cells. This metabolic disruption may further compromise their ability to support oocyte development. Previous studies have found mitochondrial dysfunction in cumulus cells in older women [46].
Interestingly, despite these metabolic impairments, genes involved in protein synthesis and ER-to-Golgi vesicle-mediated transport—such as COPII complex components and SNARE machinery—were upregulated following 4-Cgh treatment. This may reflect a compensatory response to cellular stress, as previous studies have shown that disruptions in glucose metabolism or glycosylation can trigger endoplasmic reticulum (ER) stress and activate the unfolded protein response (UPR), thereby enhancing protein processing and trafficking [47,48]. Thus, the upregulation of protein transport machinery may represent an adaptive mechanism to maintain essential functions under carbohydrate metabolic imbalance.
Previous studies have established that dysregulated glucose and lipid metabolism negatively affect oocyte quality and embryonic development in porcine and bovine species, respectively [49,50]. Our findings extend this understanding by demonstrating that PLAU inhibition impairs cumulus cell metabolism, thereby disrupting oocyte maturation.
Insulin resistance, a hallmark of polycystic ovary syndrome (PCOS), may impair glucose uptake and disrupt carbohydrate metabolism in follicular somatic cells, including cumulus cells. This metabolic imbalance could weaken the supportive function of cumulus cells and compromise the oocyte’s developmental competence. Notably, women with PCOS exhibit elevated circulating levels and activity of plasminogen activator inhibitor-1 (PAI-1), encoded by SERPINE1, which are positively correlated with insulin resistance [51]. Furthermore, recent evidence suggests that SERPINE1 mediates GDF8-induced defects in glucose metabolism in granulosa cells from patients with PCOS [52]. Given the critical role of PLAU in cumulus cell expansion and metabolic remodeling, ovulatory dysfunction in PCOS may be partially attributed to PLAU deficiency–associated disruption of carbohydrate metabolism in cumulus cells. Therefore, PLAU may act not only as a structural regulator of the cumulus-oocyte complex but also as a potential upstream modulator of metabolic homeostasis in the follicular microenvironment.

4. Materials and Methods

4.1. IVM of Mouse Oocytes

Specific pathogen-free CD-1 female mice were obtained from BioLASCO Taiwan (Yilan, Taiwan) and bred in accordance with institutional guidelines for the care and use of experimental animals. All animal experiments were approved by the Institutional Animal Care and Use Committee at MacKay Memorial Hospital (approval numbers: MMH-A-S-105-63 and MMH-A-S-107-60). The mice were housed under controlled lighting (14 h of light and 10 h of darkness) at 21–22 °C, with unrestricted access to food and water.
The mice (aged 21–24 days) were injected with 5 IU of pregnant mare serum gonadotropin (Sigma-Aldrich, St. Louis, MO, USA). After 46 h, the mice were euthanized by gradual-fill CO2 inhalation, following the guidelines of the Institutional Animal Care and Use Committee of MacKay Memorial Hospital and the 2020 AVMA euthanasia guidelines, with death confirmed by cervical dislocation [53]. Ovaries were collected into minimal essential medium-α (MEMα). Immature COCs were collected from antral follicles by puncturing the ovaries with a 23-G needle and selected under a stereomicroscope using a 145-μm micropipette. GV-stage oocytes were identified based on the morphology of the surrounding cumulus cells: only COCs with uniform size, two intact layers of cumulus granulosa cells, and compact structure were selected. While the germinal vesicle within the oocyte is not directly visible due to cumulus coverage, this morphological selection reliably identifies GV-stage oocytes, as described in previous studies [34,54].
In each independent experiment, ovaries were collected from 5 mice, yielding about 50 intact COCs. These COCs were evenly divided into four treatment groups and cultured under specific experimental conditions. The experiment outlined in Section 2.1 was repeated three times with a total of 15 mice; the experiment in Section 2.2 was repeated four times with 20 mice; the experiment in Section 2.3 was performed twice with 10 mice; and a preliminary test was performed once with 2 mice.
Additional experiments in Section 2.4 included three rounds of time-lapse imaging with 15 mice, two separate COC survival tests with 4 mice each, and gene expression analyses from 11 independent experiments, each with 4 mice (total n = 44). In total, 110 mice were used in this study. Each COC group was cultured in 100 μL of IVM medium, prepared as previously described [55,56]. The medium’s pH was maintained at 7.4, and it was covered with mineral oil. Cultures were incubated at 37 °C in 5% CO2 for 16 to 20 h.
The specific PLAU enzyme inhibitor, 4-chlorophenylguanidine hydrochloride (4-Cgh, APExBIO, Catalog No. A4445, Houston, TX, USA), was prepared in MEMα at a stock concentration of 100 mM and stored at −20 °C. Before use, it was thawed and diluted to the required experimental concentration in culture medium. In Experiment 2.2, a concentration of 1 mM 4-Cgh was used, which effectively suppressed oocyte maturation. For Experiment 2.3, considering that excessive serum supplementation could alter the concentrations of basic components in the culture medium and affect its osmotic pressure, a lower concentration of 0.8 mM 4-Cgh was selected. This concentration still effectively inhibited oocyte maturation, as reflected in cumulus expansion and oocyte maturation outcomes (Table S1). PLAU protein was purchased from Sigma-Aldrich (Cat. No. CC4000) and diluted to the working concentration in culture medium. In serum adjustment experiments, serum was added or removed as required for each group.

4.2. Assessment of Oocyte Maturation Rate

At the end of the culture, 150 IU of hyaluronidase (Sigma-Aldrich, Cat. No. H3506) was added to the medium, and the mixture was incubated at 37 °C for 5 min to dissociate the cumulus cells. Oocytes were examined under a stereomicroscope using a 145-μm micropipette (EZ-Strip, Windsor, ON, Canada) to assess their maturation status. Oocytes with a visible nucleus were identified as immature (GV stage), whereas those with an extruded first polar body and a distinct perivitelline space were considered mature metaphase II oocytes.

4.3. PLAU Enzyme Activity Analysis

PLAU activity was assessed using the uPA Activity Assay Kit (Cat. No. ECM600, EMD Millipore, Burlington, MA, USA). Note that uPA is a common name for PLAU. The positive control included with the kit was reconstituted with 1 mL of deionized water, mixed thoroughly, aliquoted, and stored at −70 °C. In the experiment, recombinant human PLAU (final concentration 5 IU) and various concentrations of 4-Cgh (0, 0.15, 0.3, 0.5, 0.8, and 1.0 mM) were added to the reaction solution and incubated at 37 °C for 1 h to inhibit the reaction. After incubation, 10–160 μL of each sample was transferred to 96-well plates, and the volume was adjusted to 160 μL with deionized water. Then, 20 μL of assay buffer was added. The chromogenic substrate was reconstituted with 2 mL of deionized water and stored at 2–8 °C. Twenty microliters of the substrate was added to each well, and the mixture was incubated at 37 °C for 1 h. Absorbance was measured at 405 nm using a microplate reader (TECAN Infinite® 200 PRO, Männedorf, Switzerland), and PLAU activity was quantified from the standard curve.

4.4. Track the Expansion of COCs During IVM Using Time-Lapse Imaging

COCs were photographed hourly at 100X magnification under low-light conditions using a Lumascope 720 (Etaluma, San Diego, CA, USA) with Lumaview 600/700 series software, resulting in a total recording time of 16 h. The distribution of COCs in the microscope field determined the number of images captured to ensure complete detection. ImageJ (1.48v, National Institutes of Health, Bethesda, MD, USA) was used to measure the diameter of each COC and to analyze changes in COC diameter over time.

4.5. COC Viability Assay

COCs were cultured in IVM medium as described above. Based on observations of cumulus expansion dynamics, the sixth hour of culture was selected as the time point for assessing cumulus cell viability and for subsequent molecular analyses. This time point corresponds to the first significant divergence between experimental and control groups. Because changes in gene expression represent upstream cellular events, this earliest detectable phenotypic difference was considered a suitable baseline for analysis.
At this time point, cell viability of COCs was assessed using the LIVE/DEAD® kit (Cat. No. L3224; Thermo Fisher Scientific, Waltham, MA, USA). First, samples containing COCs (including the control and 1 mM 4-Cgh groups) were removed from the culture dishes. Then, 100 μL of thawed, pre-prepared 2X calcein AM (component A) and ethidium homodimer-1 (component B) in IVM culture medium were added to each sample. Next, samples were incubated at 37 °C in 5% CO2 for 30 min. After incubation, fluorescence microscopy was used to identify live cells (green; excitation/emission wavelengths 494/517 nm) and dead cells (red; excitation/emission wavelengths 528/617 nm), and images were captured for further analysis.

4.6. Collection of Cumulus Cell Samples

After six hours of culture, COCs were collected under a stereomicroscope and transferred to an Eppendorf tube. The samples were vortexed for 30 s to detach the cumulus cells from the oocytes, after which they were subsequently transferred to a new dish. Using a micropipette, the oocytes were carefully picked out, avoiding suction of the cumulus cells. The remaining cumulus cells were collected into a separate tube and centrifuged at 20,000× g for 10 min. The supernatant was discarded, and the pellet was resuspended in the lysis buffer provided in the RNA extraction kit. The mixture was vortexed for 1 min, incubated at room temperature for 10 min, and then stored at −80 °C. A total of 22 samples were collected (11 experimental and 11 control), each containing 20–30 oocytes’ surrounding cumulus cells from 2 mice.

4.7. Total RNA Extraction

Total RNA was isolated using the RNeasy Kit (Cat. No. 74104; Qiagen, Hilden, Germany) according to the manufacturer’s instructions. RNA was eluted in 25 μL of UltraPure distilled water (Invitrogen, Carlsbad, CA, USA). RNA concentration was measured using a Qubit 4 Fluorometer (Invitrogen). RNA quality was assessed by microfluidic electrophoresis using the Agilent 2100 Bioanalyzer with the RNA 6000 Nano Kit (Agilent Technologies). 1 μL of each RNA sample was loaded for analysis. RNA integrity was evaluated using the 18S/28S ribosomal RNA peak ratio and the overall fragmentation profile, and only samples with an RNA integrity score greater than 7 were selected for further experiments. RNA samples were stored at −80 °C until further use.

4.8. RNA Sequencing

RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Beijing, China) with an RNA 6000 Nano Kit. The samples were diluted to 5 ng/μL, and 50 ng was used for library preparation with the Universal Plus mRNA-Seq Kit (Nugen, Redwood City, CA, USA). Library concentration and quality were measured using a Qubit® 2.0 Fluorometer (Invitrogen) with a dsDNA High Sensitivity Kit, and a DNA 1000 assay on an Agilent 2100 Bioanalyzer. Sequencing was performed on a HiSeq 4000 system (Illumina, San Diego, CA, USA) with 2 × 150 bp paired-end reads. Low-quality reads were filtered to retain only high-quality sequences for analysis.

4.9. Bioinformatic Analysis

Trimmed paired-end reads were aligned to the Mus musculus GRCm38 reference genome using CLC Genomics Workbench v10 (Qiagen). Gene expression levels were quantified using reads per kilobase of exon model per million mapped reads (RPKM). This metric represents the number of reads mapped to a specific gene, normalized by the total exon length (in kilobases) and the number of mapped reads (in millions). RNA-seq data from this study have been deposited in the Sequence Read Archive database under accession number PRJNA1232949. Differentially expressed genes (DEGs) were identified using Cuffdiff (v2.2.1) based on a negative binomial distribution model. Genes with an adjusted p-value < 0.05 and a fold change ≥ 1.5 were considered significant. Heatmaps were generated using Z-score-transformed RPKM values of DEGs with a fold change of ≥1.5 and a p-value of ≤0.05. A heatmap and a volcano plot were generated using https://www.bioinformatics.com.cn (accessed on 24 July 2025), an online platform for data analysis and visualization.

4.10. Gene Ontology (GO) Enrichment and Protein–Protein Interaction (PPI) Network Analysis

Genes with relatively high expression (RPKM > 15) were analyzed using the STRING database (version 12; https://string-db.org/, accessed on 16 October 2025), which integrates known and predicted protein–protein interactions from both experimental and computational evidence. GO enrichment analysis was performed using the built-in functional enrichment tool in STRING, with the Biological Process category selected. Default parameters were used, and significant terms were identified at a false discovery rate (FDR) < 0.05.
To use more comprehensive human annotation data and compare or infer human biological pathways, all mouse genes were converted to human ortholog IDs (ortholog mapping), and pathway enrichment analysis was conducted using the human database.
The resulting PPI network was exported from STRING and imported into Cytoscape (version 3.10.3) for visualization. In the network, node color indicates gene-expression changes (red for upregulated and blue for downregulated genes), node size reflects connectivity or centrality, and edge width indicates interaction confidence scores from STRING. Functional clusters or GO terms were manually annotated and visually grouped by biological category.

4.11. Quantitative Real-Time PCR (qPCR)

Eight DEGs were selected for validation by qPCR. Primers were designed using CLC Main Workbench 7 (Qiagen). qPCR was performed using the Power SYBR™ Green RNA-to-CT™ 1-Step Kit (Applied Biosystems, Carlsbad, CA, USA) according to the manufacturer’s instructions. The 20 μL reactions contained 1 ng of RNA, 5 μM forward and reverse primers, 1X RT Enzyme Mix, 1X Power SYBR® Green RT-PCR Mix, and RNase-free H2O. The cycling conditions were: 48 °C for 30 min, 95 °C for 10 min, then 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Melt curve analysis was also performed. Relative gene expression was calculated using the 2−ΔΔCT method and normalized to Rpl13a as an internal control [57,58]. Primer sequences are listed in Table S2.

4.12. Statistical Analysis

All statistical analyses were performed using GraphPad Prism version 9.3.1 (GraphPad Software, San Diego, CA, USA). Data from three independent experiments were analyzed. For each experiment, the COC expansion diameter and oocyte maturation rate were calculated for each treatment group. Since oocyte maturation rates are expressed as percentages and certain treatment groups approach extreme values (0% or 100%), the data were initially transformed with the arcsine square root to stabilize variance and meet the assumptions of the tests. After transformation, the data were analyzed using one-way ANOVA followed by Tukey’s post hoc test. Data are presented as mean ± SEM, with each data point representing one independent experiment. Differences were considered statistically significant at p < 0.05.

5. Conclusions

This study is the first to comprehensively investigate the regulatory role of PLAU in oocyte maturation using RNA-seq. Our findings show that PLAU is crucial not only for extracellular matrix remodeling during cumulus expansion but also for maintaining the metabolic competence of cumulus cells, both of which are critical for supporting the oocyte’s developmental potential. These results provide novel mechanistic insights into the PLAU–cumulus–oocyte axis, highlighting PLAU as a promising molecular target for improving IVM culture systems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27041781/s1.

Author Contributions

Conceptualization, Y.-M.H., and S.-H.L.; methodology, L.-Y.Y., C.S.-C.C., and S.-H.L.; software, L.-Y.Y.; validation, L.-Y.Y., and S.-H.L.; investigation, L.-Y.Y., and C.S.-C.C.; resources, R.K.-K.L., M.-H.L.; data curation, L.-Y.Y., C.S.-C.C., and K.-S.L.; writing—original draft preparation, L.-Y.Y.; and S.-H.L.; writing—review and editing, L.-Y.Y.; and S.-H.L.; supervision, S.-H.L.; funding acquisition, L.-Y.Y., and Y.-M.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the National Science and Technology Council, Taiwan ROC (Grant #106-2314-B-195-014 and #112-2314-B-195-027). It was also funded by MacKay Memorial Hospital (Grant #MMH 107-09 and 113-121), Taipei, Taiwan, ROC.

Institutional Review Board Statement

The study was conducted in accordance with institutional guidelines for the care and use of experimental animals. All animal experiments were approved by the Institutional Animal Care and Use Committee at MacKay Memorial Hospital (approval numbers: MMH-A-S-105-63 and MMH-A-S-107-60; approval date: 24 December 2025).

Informed Consent Statement

Not applicable.

Data Availability Statement

RNA sequencing data generated in this study are available in the Sequence Read Archive under accession number PRJNA1232949.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the study design, data collection, analysis, or interpretation, manuscript preparation, or the decision to publish the results.

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Figure 1. Inhibitory effect of 4-Cgh on PLAU activity. Effect of different concentrations of 4-Cgh (0, 0.15, 0.3, 112, 0.5, 0.8, and 1 mM) on PLAU enzyme activity. The residual PLAU enzyme activity is displayed on the Y-axis.
Figure 1. Inhibitory effect of 4-Cgh on PLAU activity. Effect of different concentrations of 4-Cgh (0, 0.15, 0.3, 112, 0.5, 0.8, and 1 mM) on PLAU enzyme activity. The residual PLAU enzyme activity is displayed on the Y-axis.
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Figure 2. Effect of the PLAU inhibitor 4-Cgh on cumulus expansion and oocyte maturation. (a–d) Representative images of immature cumulus–oocyte complexes (COCs) cultured for 16 h with increasing concentrations of 4-Cgh. Scale bar = 100 μm. (e) Quantification of COC diameter. Dots represent individual COCs; black lines indicate the mean ± SD. **** p < 0.0001 compared with the control (CTR) group. (f) Number and proportion of oocytes maturing to metaphase II (MII) of meiosis. Data are presented as mean ± SD from three independent experiments, with 9–15 oocytes per group. Different letters indicate statistically significant differences between groups (a vs. CTR; b vs. 1 mM 4-Cgh). † denotes the oocyte culture outcome for each independent experiment. The denominator represents the total COCs cultured, and the numerator indicates the number of mature oocytes. The results shown here are from three replicate experiments. MII indicates a mature oocyte, identified primarily by microscopic examination of nuclear maturity. The key indicator is the extrusion of the first polar body.
Figure 2. Effect of the PLAU inhibitor 4-Cgh on cumulus expansion and oocyte maturation. (a–d) Representative images of immature cumulus–oocyte complexes (COCs) cultured for 16 h with increasing concentrations of 4-Cgh. Scale bar = 100 μm. (e) Quantification of COC diameter. Dots represent individual COCs; black lines indicate the mean ± SD. **** p < 0.0001 compared with the control (CTR) group. (f) Number and proportion of oocytes maturing to metaphase II (MII) of meiosis. Data are presented as mean ± SD from three independent experiments, with 9–15 oocytes per group. Different letters indicate statistically significant differences between groups (a vs. CTR; b vs. 1 mM 4-Cgh). † denotes the oocyte culture outcome for each independent experiment. The denominator represents the total COCs cultured, and the numerator indicates the number of mature oocytes. The results shown here are from three replicate experiments. MII indicates a mature oocyte, identified primarily by microscopic examination of nuclear maturity. The key indicator is the extrusion of the first polar body.
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Figure 3. Effects of culture medium components on COC diameter and oocyte maturation during IVM. Light blue: 10% serum; light green: serum-free; dark blue: 10% serum + 1 mM 4-Cgh; dark green: serum-free + 2 U PLAU. (a) COC diameter after 16 h of culture (dots = individual COCs; black line = mean ± SD). *** p < 0.001, **** p < 0.0001. (b) Number and proportion of oocytes maturing to metaphase II (MII) of meiosis. Data are presented as mean ± SD from four independent experiments, with 6–17 oocytes per group. Different letters indicate statistically significant differences between groups (a vs. CTR; b vs. serum-free; c vs. 10% serum + 4-Cgh). † denotes the oocyte culture outcome for each independent experiment. The denominator represents the total COCs cultured, and the numerator indicates the number of mature oocytes. The results shown here are from three replicate experiments. MII indicates a mature oocyte, identified primarily by microscopic examination of nuclear maturity. The key indicator is the extrusion of the first polar body.
Figure 3. Effects of culture medium components on COC diameter and oocyte maturation during IVM. Light blue: 10% serum; light green: serum-free; dark blue: 10% serum + 1 mM 4-Cgh; dark green: serum-free + 2 U PLAU. (a) COC diameter after 16 h of culture (dots = individual COCs; black line = mean ± SD). *** p < 0.001, **** p < 0.0001. (b) Number and proportion of oocytes maturing to metaphase II (MII) of meiosis. Data are presented as mean ± SD from four independent experiments, with 6–17 oocytes per group. Different letters indicate statistically significant differences between groups (a vs. CTR; b vs. serum-free; c vs. 10% serum + 4-Cgh). † denotes the oocyte culture outcome for each independent experiment. The denominator represents the total COCs cultured, and the numerator indicates the number of mature oocytes. The results shown here are from three replicate experiments. MII indicates a mature oocyte, identified primarily by microscopic examination of nuclear maturity. The key indicator is the extrusion of the first polar body.
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Figure 4. Effects of elevated serum concentration on COC diameter and oocyte maturation with 0.8 mM 4-Cgh during IVM. Light blue: 10% serum; dark blue: 10% serum + 4-Cgh; brown: 15% serum + 4-Cgh; red: 20% serum + 4-Cgh. (a) COC diameter after 16 h of culture (dots = individual COCs; black lines = mean ± SD). **** p < 0.0001. (b) Number and proportion of oocytes maturing to metaphase II (MII) of meiosis. Data are presented as mean ± SD from two independent experiments, with 9–17 oocytes per group. Statistical significance was determined by comparison with the control group (CTR); the letter “a” denotes p < 0.05 versus CTR. † denotes the oocyte culture outcome for each independent experiment. The denominator represents the total COCs cultured, and the numerator indicates the number of mature oocytes. The results shown here are from three replicate experiments. MII indicates a mature oocyte, identified primarily by microscopic examination of nuclear maturity. The key indicator is the extrusion of the first polar body.
Figure 4. Effects of elevated serum concentration on COC diameter and oocyte maturation with 0.8 mM 4-Cgh during IVM. Light blue: 10% serum; dark blue: 10% serum + 4-Cgh; brown: 15% serum + 4-Cgh; red: 20% serum + 4-Cgh. (a) COC diameter after 16 h of culture (dots = individual COCs; black lines = mean ± SD). **** p < 0.0001. (b) Number and proportion of oocytes maturing to metaphase II (MII) of meiosis. Data are presented as mean ± SD from two independent experiments, with 9–17 oocytes per group. Statistical significance was determined by comparison with the control group (CTR); the letter “a” denotes p < 0.05 versus CTR. † denotes the oocyte culture outcome for each independent experiment. The denominator represents the total COCs cultured, and the numerator indicates the number of mature oocytes. The results shown here are from three replicate experiments. MII indicates a mature oocyte, identified primarily by microscopic examination of nuclear maturity. The key indicator is the extrusion of the first polar body.
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Figure 5. COC diameter dynamics and survival during IVM. (a) Time-lapse images of COC diameter changes were analyzed in ImageJ—red: control; blue: 4-Cgh; green: average difference. Data are mean ± SD (n = 8–14 per group, three experiments). **** p < 0.0001 vs. CTR. (b,c) COC survival after 6 h IVM assessed by LIVE/DEAD staining (green: live, red: dead). Scale bar: 100 μm.
Figure 5. COC diameter dynamics and survival during IVM. (a) Time-lapse images of COC diameter changes were analyzed in ImageJ—red: control; blue: 4-Cgh; green: average difference. Data are mean ± SD (n = 8–14 per group, three experiments). **** p < 0.0001 vs. CTR. (b,c) COC survival after 6 h IVM assessed by LIVE/DEAD staining (green: live, red: dead). Scale bar: 100 μm.
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Figure 6. Gene expression analysis of cumulus cells in the 4-Cgh-treated and control groups. (a) PCA shows a separation between the 4-Cgh-treated group (red dots) and the control group (blue dots), with sample IDs indicated. (b) Volcano plot of DEGs between 4-Cgh and CTR (adjusted p < 0.05, |FC| > 1.5). Red: upregulated; Blue: downregulated. (c) An unsupervised hierarchical clustering heatmap of 1340 DEGs indicates upregulated genes in red and downregulated genes in blue. (d) Validation of selected DEGs using qRT-PCR (blue dots) compared to RNA-seq results (green dots), with fold change calculated relative to control (RNA-seq, n = 11; qPCR, n = 11).
Figure 6. Gene expression analysis of cumulus cells in the 4-Cgh-treated and control groups. (a) PCA shows a separation between the 4-Cgh-treated group (red dots) and the control group (blue dots), with sample IDs indicated. (b) Volcano plot of DEGs between 4-Cgh and CTR (adjusted p < 0.05, |FC| > 1.5). Red: upregulated; Blue: downregulated. (c) An unsupervised hierarchical clustering heatmap of 1340 DEGs indicates upregulated genes in red and downregulated genes in blue. (d) Validation of selected DEGs using qRT-PCR (blue dots) compared to RNA-seq results (green dots), with fold change calculated relative to control (RNA-seq, n = 11; qPCR, n = 11).
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Figure 7. PPI network of 255 DEGs visualized by STRING and Cytoscape. Node color (red = up, blue = down; intensity = fold change), size (connectivity), and edge darkness (interaction confidence).
Figure 7. PPI network of 255 DEGs visualized by STRING and Cytoscape. Node color (red = up, blue = down; intensity = fold change), size (connectivity), and edge darkness (interaction confidence).
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Figure 8. GO enrichment (biological process) of 246 STRING network proteins. X-axis: log10(observed/expected); circle size: number of proteins; color intensity: FDR significance.
Figure 8. GO enrichment (biological process) of 246 STRING network proteins. X-axis: log10(observed/expected); circle size: number of proteins; color intensity: FDR significance.
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Figure 9. Gene-expression trends in key biological pathways following 4-Cgh treatment. The heatmap displays gene expression patterns across canonical glycolysis (a), the ovulatory cycle (b), carbohydrate derivative metabolism (c), protein transport (d), and ER-to-Golgi vesicle-mediated transport (e). Red: upregulation; green: downregulation.
Figure 9. Gene-expression trends in key biological pathways following 4-Cgh treatment. The heatmap displays gene expression patterns across canonical glycolysis (a), the ovulatory cycle (b), carbohydrate derivative metabolism (c), protein transport (d), and ER-to-Golgi vesicle-mediated transport (e). Red: upregulation; green: downregulation.
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Yeh, L.-Y.; Chiu, C.S.-C.; Lee, K.-S.; Lee, R.K.-K.; Lin, M.-H.; Hwu, Y.-M.; Li, S.-H. Transcriptomic Profiling Reveals Urokinase-Type Plasminogen Activator-Mediated Regulation of Metabolic Competence and Cumulus Expansion During Mouse Oocyte In Vitro Maturation. Int. J. Mol. Sci. 2026, 27, 1781. https://doi.org/10.3390/ijms27041781

AMA Style

Yeh L-Y, Chiu CS-C, Lee K-S, Lee RK-K, Lin M-H, Hwu Y-M, Li S-H. Transcriptomic Profiling Reveals Urokinase-Type Plasminogen Activator-Mediated Regulation of Metabolic Competence and Cumulus Expansion During Mouse Oocyte In Vitro Maturation. International Journal of Molecular Sciences. 2026; 27(4):1781. https://doi.org/10.3390/ijms27041781

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Yeh, Ling-Yu, Christine Shan-Chi Chiu, Kuan-Sheng Lee, Robert Kuo-Kuang Lee, Ming-Huei Lin, Yuh-Ming Hwu, and Sheng-Hsiang Li. 2026. "Transcriptomic Profiling Reveals Urokinase-Type Plasminogen Activator-Mediated Regulation of Metabolic Competence and Cumulus Expansion During Mouse Oocyte In Vitro Maturation" International Journal of Molecular Sciences 27, no. 4: 1781. https://doi.org/10.3390/ijms27041781

APA Style

Yeh, L.-Y., Chiu, C. S.-C., Lee, K.-S., Lee, R. K.-K., Lin, M.-H., Hwu, Y.-M., & Li, S.-H. (2026). Transcriptomic Profiling Reveals Urokinase-Type Plasminogen Activator-Mediated Regulation of Metabolic Competence and Cumulus Expansion During Mouse Oocyte In Vitro Maturation. International Journal of Molecular Sciences, 27(4), 1781. https://doi.org/10.3390/ijms27041781

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