PDK4 is critical for metabolic regulation and progesterone production in bovine granulosa cells

Abstract

Non-esterified fatty acids (NEFAs) influence ovarian cell function, but the underlying molecular mechanisms remain incompletely understood. Our previous work showed that NEFA induces lipid accumulation and inhibits glucose uptake in bovine granulosa cells. Here, we investigated the effect of NEFA on energy metabolism and the transcriptome, with a focus on pyruvate dehydrogenase kinase 4 (PDK4). NEFA treatment significantly increased ATP levels and mitochondrial membrane potential. Transcriptome analysis revealed differential expression of 176 genes, with downregulation of steroidogenesis and fatty acid synthesis pathways, and upregulation of apoptosis, fatty acid oxidation, and innate immune responses in NEFA treated cells. Specifically, genes involved in fatty acid oxidation (e.g. CPT1A, CPT1B, HADHA, and SLC25A20) were upregulated, whereas those related to glucose metabolism remained largely unchanged except for the marked upregulation of PDK4. Silencing PDK4 expression induced glucose utilization by upregulating glucose transporters (SLC2A1, SLC2A10) and glycolytic enzymes (GAPDH, ENO1, and LDHA). In contrast, genes associated with fatty acid oxidation (SLC27A1 and CPT1B) showed downregulation upon PDK4 silencing compared with controls, suggesting a metabolic shift favoring glucose oxidation even in the presence of NEFA. We observed that expression of PDK4, CPT1A, and CPT1B was significantly upregulated in large luteal cells compared to granulosa cells. PDK4 knockdown significantly downregulated steroidogenic genes (STAR, HSD3B1, and CYP11A1) and decreased progesterone production, suggesting that increased expression of PDK4 in luteal cells may supports steroidogenesis. Together, these findings identify PDK4 as a critical regulator of metabolic flexibility and progesterone production in granulosa cells.

This study evaluates the transcriptome changes induced by NEFA in granulosa cells and identifies pyruvate dehydrogenase kinase 4 (PDK4) as a key regulator of metabolic reprogramming and steroidogenesis in bovine granulosa cells, linking altered energy metabolism to ovarian cell function.

Graphical Abstract

Graphical Abstract.

Introduction

Despite being metabolically opposite, obesity and negative energy balance (NEB) both could contribute to the reduced fertility rates in females, partly due to elevated levels of non-esterified fatty acids (NEFA) in blood and in ovarian follicular fluid. High yielding dairy cows mobilize body fat reserves due to NEB during the postpartum period, which is a well-known bottleneck for fertility that contributes to economic losses in the dairy sector [1, 2]. It has been described that oleate (OA, C18:1), palmitate (PA, C16:0), and stearate (SA, C18:0) are the predominant fatty acids in circulation and in follicular fluid due to their de novo synthesis in the body and also their abundance in the diet [3–6]. Numerous studies have documented the negative effects of NEFA on granulosa cells and oocyte developmental competence in different species as reviewed here [7]. Mu et al (2001) reported that PA and SA induced human granulosa cell apoptosis in a time and dose dependent manner [8]. PA, SA, and OA have been shown to inhibit bovine granulosa cell proliferation in a varying degree depending on their concentration in vitro [9]. During in vitro maturation (IVM), exposure of bovine and porcine oocytes to PA or SA had negative effects on fertilization, cleavage, and blastocyst rates [10, 11]. Jorritsma et al (2004) demonstrated that high levels of OA (1 mM) in IVM media delayed the progression of meiosis, lowered fertilization, and embryo development rate [12]. It was found that cumulus cells store NEFA in lipid droplets and can protect the oocyte from NEFA induced stress in bovine [4].

Earlier, we demonstrated that saturated and unsaturated fatty acid elicit opposite effects in bovine granulosa cells. Particularly, OA inhibits while PA and SA stimulate the estradiol production of bovine granulosa cells in vitro [13]. Interestingly, the mix of OA, PA, and SA, which mediates the metabolic stress physiologically, did not induce negative effects on estradiol production [14]. On the other hand, PA and SA did not significantly reduce progesterone production of bovine granulosa cells; however, they downregulated STAR (steroidogenic acute regulatory protein), which is critical for cholesterol transport into mitochondria, while simultaneously upregulating HSD3B, the enzyme catalyzing the conversion of pregnenolone to progesterone [13]. In contrast, OA suppressed both STAR and HSD3B1 expression, leading to a reduction in progesterone production. Interestingly, exposure to the mixture of PA, SA, and OA downregulated STAR expression without affecting HSD3B and despite the downregulation of STAR, overall progesterone production was not significantly altered by the NEFA mix [13]. In another study, we analyzed the glucose metabolism in granulosa cells in response to OA, PA, and SA and their combination [15]. We have shown that OA induced dramatic glucose consumption while PA and SA did not show any effect. Contrary to the OA induced effects, a mixture of OA, PA, and SA inhibited the glucose consumption in granulosa cells, which can be attributed to the surplus energy supply from the oxidation of saturated fatty acids.

The PDK4 gene encodes for an enzyme called pyruvate dehydrogenase kinase 4. It phosphorylates and thereby inhibits the pyruvate dehydrogenase complex (PDC), which when activated converts the glycolytic product pyruvate into acetyl CoA to be consumed in the tricarboxylic acid (TCA) cycle in the mitochondria. This action of PDK4 diverts glucose derived carbons from the TCA cycle to other fates such as lactate or gluconeogenesis and promotes fatty acid oxidation [16]. In the liver, inhibition of PDC by PDK4 helps direct pyruvate toward gluconeogenesis rather than being oxidized for energy production in mitochondria. The expression of PDK4 is highly regulated in the cells according to the physiological conditions. In the well-fed state, PDK4 expression is typically low, allowing PDC to remain active and promote glucose oxidation. In contrast, during fasting or metabolic stress, PDK4 is strongly upregulated [16]. For example, 40-h fasting in humans resulted in a 14-fold increase in skeletal muscle PDK4 mRNA expression, accompanied by a significant decrease in PDC activity [17]. This fasting-induced PDK4 upregulation preserves pyruvate for gluconeogenesis and facilitates fatty acid oxidation. Gene deficiency or chemical inhibition of PDK4 increases PDC activity and accelerates glucose oxidation [18]. In PDK4 knockout mice, muscle cells oxidized glucose much faster than in wild-type mice, even abolishing the usual fatty acid mediated suppression of glucose oxidation [18]. This indicates the pivotal role played by PDK4 in cellular metabolism, particularly in fuel flux from glucose to fatty acids.

In the current study, we performed global gene expression analysis of primary bovine granulosa cells in response to the mixture of OA, PA and SA compared to the control. We then analyzed the effect of PDK4 gene, which is upregulated in NEFA treated cells, on the cellular energy metabolism and lipid accumulation and steroidogenesis, revealing the importance of PDK4 gene expression in metabolic flexibility and steroidogenesis of granulosa cells.

Materials and methods

Collection and in vitro culture of primary bovine granulosa cells

Ethics statement: Ovaries were collected post-mortem from cattle at a licensed abattoir (DANISH CROWN Teterower Fleisch GmbH, Germany). As no procedures were performed on live animals, formal animal ethics approval was not required.

The isolation and culture of bovine granulosa cells have been described previously by us [14, 15, 19–21]. Briefly, bovine ovaries were obtained from a local abattoir and transported to the laboratory in a phosphate-buffered saline (PBS) solution containing penicillin (100 IU/mL), streptomycin (100 μg/mL) and 0.5 μg/μL amphotericin. Granulosa cells were manually aspirated from 2 to 6 mm sized ovarian follicles with clear follicular fluid using a 3 mL syringe and 18G needles. Viable cells were counted via the trypan blue exclusion method, and cells were cryopreserved in a freezing medium made of fetal calf serum containing 10% DMSO. For cell culture, cryopreserved vials were thawed in a water bath at 37°C and cells were washed using αMEM by centrifugation at 500 g for 3 min. The cell pellet was dissolved in αMEM containing 2 mM glutamine, 10 mM sodium bicarbonate, 20 mM HEPES, 4 ng/mL sodium selenite, 0.1% w/v BSA, 5 μg/mL transferrin, 10 ng/mL insulin, 1 mM non-essential amino acids, 100 IU/mL penicillin and 0.1 mg/mL streptomycin. Androstenedione (2 mM), FSH (20 ng/mL) and/or R3 IGF1 (50 ng/mL) was added to the media on the day of culture. The cells were seeded at a density of 5 × 10⁴ cells per well in 48-well culture dishes, pre-coated with 0.02% collagen R (Serva, Germany) to promote healthy attachment. Fatty acids were added to the cultured granulosa cells on days 2 and 4 by replacing 70% of the spent medium with a fresh medium containing fatty acids and cells were analyzed on day 6. All cell cultures were maintained at 37°C and 5% CO₂ in a humidified incubator.

For silencing the PDK4 expression, negative control (sham) GapmeR (A^*A^*C^*A^*C^*G^*T^*C^*T^*A^*T^*A^*C^*G^*C) and antisense PDK4 GapmeR oligos (C^*G^*C^*T^*G^*C^*T^*C^*G^*T^*C^*T^*G^*A^*T^*A), were applied using the TransIT-X2 transfection reagent (Mirus Bio, USA) on day 2 of culture. The medium was replaced with fresh medium containing fatty acids on days 4 and 6, and cells were harvested on day 8.

Fatty acids preparation

All fatty acids (OA, PA, and SA) were prepared using our previously published method [13, 15]. Palmitic acid (PA, Cat. #P0500, Sigma) and stearic acid (SA, Cat. #S4751, Sigma) were obtained as solids, they were first dissolved in chloroform/methanol solution (v/v = 2/3) and dried under a nitrogen gas chamber. Oleic acid (OA, Cat. #O1008, Sigma) was supplied in liquid form. Fatty acids were dissolved in PBS with 10% fatty acid-free bovine serum albumin (BSA) to prepare 5 mM solutions. The resulting solutions contain a fatty acid to BSA molar ratio of 3.3. All fatty acid solutions were vortexed, sonicated and then placed in a water bath at 37°C overnight to form a clear solution. The solutions were filtered through a 0.22 μm filter and stored at −20°C in aliquots. PBS with 10% fatty acid-free BSA without fatty acids were used as control for NEFA treated cells.

Flow cytometry quantification

Cultured granulosa cells were detached by incubating with 200 μL of ready-to-use Accutase (A6964, Sigma-Aldrich, Germany) for 20 min at 37°C. Cells were collected by centrifugation at 300 × g for 5 min in 1.5 mL tubes and washed twice with pre-warmed DMEM (37°C). Subsequently, for ATP quantification, cells were incubated with BioTracker ATP-Red Live Cell Dye (1:400 dilution in DMEM, SCT045, Sigma-Aldrich, Germany) for 30 min at 37°C. For mitochondrial membrane potential (MMP) analysis, detached cells were incubated with MitoTracker Orange CMTMRos (300 nM in DMEM; M7510, ThermoFisher Scientific). For lipid droplet accumulation, cells were incubated with BioTracker 488 Green Lipid Droplet Dye (1:400 in DMEM; SCT144, Sigma-Aldrich) for 30 min at 37°C. Prior to analysis by flow cytometry, propidium iodide (PI) was added to the cells at a final concentration of 5 μg/ml to mark dead cells. During the flow analysis, cells were excited by a 488 nm laser and the emission was recorded at 488 nm for lipid droplet, 561 nm for Mitotracker, 576 nm for ATP and 620 ± 30 nm for PI. The fluorescence signal of single cells (10 000 counts) was quantified using flow cytometry (Gallios, Beckman-Coulter) and the data were analyzed with Kaluza 1.2 software (www.beckman.de).

Transcriptome analysis

Transcriptome analysis was conducted using Bovine Gene 1.0 ST Arrays (Affymetrix, USA). Total RNA from BSA- and NEFA-treated cells was extracted using the RNeasy Mini Kit (Qiagen, Germany). RNA integrity was assessed with a Bioanalyzer (Agilent Technologies, USA), yielding RIN values between 9.3 and 9.7 for all the samples, indicating high-quality and intactness of RNA. Amplification and labeling were performed using the GeneChip 3′ Amplification One-Cycle Target Labeling and Control Reagents (Affymetrix) according to the manufacturer’s protocol. Hybridization was perfformed overnight in the GeneChip Hybridization Oven (Affymetrix), and arrays were scanned using the GeneChip Scanner 3000 (Affymetrix). Data analysis was conducted using Transcriptome Analysis Console 4.0 (TAC 4.0, Affymetrix, https://www.thermofisher.com) to identify differentially expressed genes. Statistical significance was determined using analysis of variance (ANOVA), with P-values adjusted for multiple testing using the false discovery rate (FDR) correction by the Benjamini–Hochberg method. Genes with a fold change ≥ |1.5| and FDR < 0.05 were considered significantly differentially expressed in the NEFA vs. control comparison. Enriched cellular functions and upstream regulators were identified through ingenuity pathway analysis (IPA).

Glucose measurement

The conditioned media from the cell cultures were collected and stored −20°C. Glucose levels in the conditioned media were determined using a commercial kit (Glucose GOD-PAP: LT-GL 0251, Labor & Technik Eberhard Lehmann, Berlin, Germany) according to the manufacturer’s protocol. Briefly, 2.5 μl of distilled water, glucose standard, and controls (normal) were pipetted into separate wells of a 96-well plate. Then 2.5 μl of conditioned media was added to the other wells, and finally 250 μl of substrate was added to all the wells. The plate was incubated at 37°C for 10 min, then the absorbance was measured at 546 nm.

RNA extraction, cDNA reverse transcription, and gene expression analysis

Total RNA was extracted from cultured cells using the innuPREP RNA Mini Kit (Analytik Jena, Germany) according to the manufacturer’s instructions. RNA concentration was determined using a NanoDrop 1000 Spectrophotometer (Thermo Scientific, Germany), and cDNA synthesis was performed with the SensiFAST cDNA Synthesis Kit (Bioline, Germany). Gene expression of specific mRNAs was analyzed by qPCR using the SensiFAST SYBR No-ROX Kit (Bioline). The specific primer pairs used are listed in Table 1. Reactions were set up in duplicate in a total volume of 12 μL and run on a LightCycler 96 Instrument (Roche, Germany). External standards for each target gene were generated via pGEM T-Vector cloning and confirmed by sequencing. Five serial dilutions of each standard (ranging from 5 × 10⁻¹² to 5 × 10⁻¹⁶ g DNA/reaction) were freshly prepared and included in each run. Melting curve analysis was performed post-run to verify product specificity, and PCR products were further confirmed by 3% agarose gel electrophoresis. Gene expression was normalized using RPLP0 as a house keeping gene.

Table 1.

qPCR primer list

Gene	Forward	Reverse	NCBI accession no.
RPLP0	TGG​TTA​CCC​AAC​CGT​CGC​ATC​TGT​A	CAC​AAA​GGC​AGA​TGG​ATC​AGC​CAA​G	NM_001012682
PDK4	TCCGCTGGCTGGTTTTGGTTACGGC	AGCAGCCCTGGCACAGACCCACTT	NM_001101883.1
SLC2A1	CATGACCATCGCGCTGGCGCTGC	AAGACGTAGGGTCCGCACAGTTGCTCC	NM_174602.2
SLC2A10	GGCTTTGGACCCGTGACCTGGCTT	TGAAGCCCAGGCCGAAGACAGCAG	NM_001192439.3
SLC27A1	TGCCATCATCGTGCACAGCAGGTACT	GCGGCAGATCTCCCCGATGTACTGG	NM_001033625.2
CD36	GCTCCTTAAGCCATTCTTGGAT	CACCAGTGTCAACGCACTTT	NM_001278621.1
CPT1B	CTCTCCACTAGCCAGATCGC	CGCTGGGCATTTGTCTCTGA	NM_001034349.2
GAPDH	AGCGAGATCCTGCCAACATCAAG	GCAGGAGGCATTGCTGACAATCT	NM_001034034.2
ENO1	TGGTGTCCGCAGCACTCTCTCCCT	TCTTGCTAACCAGGGCAGGCGCAA	NM_174049.2
STAR	TTGTGAGCGTACGCTGTACCAAG	CTGCGAGAGGACCTGGTTGATG	NM_174189.3
HSD3B1	TGTTGGTGGAGGAGAAGGATCTG	GCATTCCTGACGTCAATGACAGAG	NM_174343.3
CYP11A1	AGAGAATCCACTTTCGCCACATC	GGTCTTTCTTCCAGGTTCCTGAC	NM_176644.2

Data mining

We mined bovine gene expression arrays from the NCBI GEO repository (GSE83524) to analyze the expression of the PDK4, CPT1A, and CPT1B in freshly isolated bovine granulosa and theca cells from large follicles and from purified preparations of small and large bovine luteal cells from mature corpora lutea. Details of the cell isolation and analysis were previously published [22]. The corresponding CEL files were downloaded and analyzed as per the current transcriptome analysis described above. A fold change threshold of |1.5|, coupled with an FDR < 0.05, was used as the cutoff to identify differential expression of PDK4, CPT1A, and CPT1B genes in granulosa cells vs large luteal cells and theca cells vs small luteal cells.

Progesterone measurement

Progesterone levels were measured using an in house developed ultrasensitive competitive [3H] radioimmunoassay (RIA) [23]. Conditioned media were collected at the end of the incubation period and stored at −20°C until analysis. On the day of the assay, samples were thawed and diluted 1:50 in RIA buffer. The radiolabeled tracer, 2,4,6,7-[3H] progesterone, was obtained from American Radiolabeled Chemicals (St. Louis, USA) and dissolved in 100% ethanol. The assay employed a rabbit-raised progesterone antibody purified by affinity chromatography (GE Healthcare). Radioactivity was measured using a liquid scintillation counter (TroCarb 2900 TR; PerkinElmer) equipped with an integrated RIA-calculation program. The minimum detection limit of progesterone was 7 pg/ml with the intra- and inter-assay coefficients of variation of 7.4% and 9.8%, respectively. A standard curve was generated for each assay using serial dilutions of progesterone (Sigma-Aldrich, Germany) against the tracer. All standards and samples were analyzed in technical duplicates.

Cell counting

Cultured cells were dissociated and washed as mentioned above in the Flow cytometry quantification procedure. The cells were suspended in 100 μl of DMEM and 20 μl of cell suspension was used for the cell counting using the Multisizer 3 Coulter Counter (Beckman Coulter, Brea, CA, USA) instrument [24, 25].

Statistics

Statistical analyses and data visualization were performed using the GraphPad Prism 10.3 licensed software (www.graphpad.com). Cell culture experiments were performed in three or more independent cultures using cells aspirated from ovaries collected on different days. All cell culture experiments contain two or three culture replicates at each condition, and the average of culture replicates was used for data analysis. mRNA expression of PDK4 upon individual fatty acid treatments was analyzed using one way ANOVA with post hoc Tuckey test. Two-way ANOVA was used to evaluate repeated measures with Tukey multiple comparison tests for determining the effect of PDK4 silencing in BSA and NEFA treated cells. Paired t-tests were performed to compare the lipid droplet, MMP and viability data upon silencing the PDK4 in NEFA treatments. P < 0.05 were generally considered statistically significant and are designated with up to four asterisk symbols to inform the strength of the significant difference (^*P < 0.05; ^**P < 0.01; ^***P < 0.001; ^****P < 0.0001).

Results

NEFA increase ATP levels and mitochondrial activity

Our previous reports demonstrated that supplementation of bovine granulosa cells with a mixture of OA, PA, and SA induces lipid accumulation and inhibits glucose uptake [15] (Figure 1A), without significantly affecting the estradiol and progesterone production [13, 14]. To further investigate the effects of NEFAs, granulosa cells were treated with a mixture of OA (200 μM), PA (100 μM), and SA (100 μM) while the corresponding volume of vehicle (10% BSA) was used as vehicle control. Flow cytometry analysis revealed a significant increase in ATP levels in NEFA treated granulosa cells compared to BSA treated controls (Figure 1B and C). We then assessed the MMP using MitoTracker (CMTMRo) staining. The results indicated a clear increase in mitochondrial activity in NEFA treated cells (Figure 1D and E). PI staining revealed no significant impact on viability by the supplemented NEFA concentrations (Figure 1F and G).

Figure 1.

NEFA induced changes in bovine granulosa cells. (A) The graphical representation of NEFA induced changes in granulosa cells previously reported by our group. This graphic was generated at www.app.biorender.com. (B) Flow cytometry histograms of adenosine triphosphate (ATP) in BSA and NEFA treated cells. (C) Quantification of ATP levels (n = 3). (D) Flow cytometry histograms of mitochondrial membrane potential (MMP) in BSA and NEFA treated cells. (E) Quantification of MMP (n = 3). (F) Representative flow cytometry dot plot of propidium iodide (PI) staining in granulosa cells treated with BSA and NEFA. PI -ve cells indicate the viable cells and PI +ve cells indicate dead cells. (G) Quantification of cell viability (n = 3). Data were analyzed by paired t-test (two-tailed). “BSA” represents cells treated with 10% bovine serum albumin (vehicle control). “NEFA” represents cells treated with NEFA mixture i.e. OA (200 μM) + PA (100 μM) + SA (100 μM). Individual data points of flow cytometric data are derived from the pooled sample of two cell culture replicates from an independent experiment. n = indicates the number of independent biological replicates analyzed.

Transcriptome wide changes induced by NEFA in granulosa cells

To determine the effect of NEFA on global gene expression, microarray analysis was performed using RNA isolated from NEFA or 10% BSA (control) treated granulosa cells. As shown in Figure 2A, principal component analysis (PCA) revealed clear clustering of BSA and NEFA treated samples, indicating distinct transcriptional profiles. Data analysis revealed that 176 genes were differentially expressed (fold change ≥ |1.5| and FDR < 0.05) between the two groups, with 101 genes upregulated and 75 downregulated in NEFA treated cells (Figure 2B). The bioinformatics predictions were drawn for the differentially expressed genes using the IPA software. Results revealed significant enrichment of genes corresponding to steroid production, fatty acid synthesis and insulin sensitivity with negative Z score values, indicating the downregulation of these biological pathways. Apoptosis, oxidation of fatty acids and lipids and vasculogenesis were significantly enriched with positive Z score values, indicating their activation in NEFA treated cells (Figure 2C). Interestingly, immune processes such as innate immune response, antimicrobial, and antiviral response were also found to have positive Z score values, indicating the potential inflammatory response caused by the NEFA treatment in granulosa cells. A full list of all affected biological processes in NEFA treated granulosa cells are listed in supplementary sheet 1.

Figure 2.

NEFA induced changes in granulosa cell transcriptome. (A) PCA clustering of BSA (squares) and NEFA (dots) treated samples of the transcriptome analysis. (B) Volcano plot of microarray data generated by plotting log2 fold change values against −log10 FDR values in NEFA treated cells. (C) Significantly enriched cellular functions in NEFA treated cells. The X-axis indicates the activation score (Z score). The size of the circle indicates the gene count in the dataset corresponding to the particular biological process. The color of the circle indicates the −log10 P-values. (D–J) Expression of candidate genes involved in (D) fatty acid oxidation, (E) fatty acid biosynthesis and (F) uptake, (G) glucose uptake, (H) glucose metabolism (glycolysis), (I) endocrine reception, and (J) steroid synthesis. The gene expression values in D-J were derived from the transcriptome analysis. “BSA” represents cells treated with 10% bovine serum albumin (vehicle control). “NEFA” represents cells treated with NEFA mixture i.e. OA (200 μM) + PA (100 μM) + SA (100 μM). The symbol # was given to the differentially regulated genes based on fulfilling the criteria of FDR-adjusted P < 0.05 and fold change |1.5| from the microarray data.

To dissect the pathway specific effects of NEFA supplementation, we examined the expression of individual genes involved in lipid metabolism, glucose metabolism, and steroidogenesis in the transcriptome data. Genes associated with fatty acid oxidation, including CPT1A, CPT1B, HADHA, HADHB, and SLC25A20, were significantly upregulated following NEFA treatment (Figure 2D). In contrast, genes involved in de novo fatty acid synthesis, such as FASN and ACACA, were significantly downregulated (Figure 2E). The expression of fatty acid transporter genes SLC27A1 and CD36 was also significantly upregulated by NEFA supplementation (Figure 2F). Analysis of glucose metabolism genes revealed no significant changes in SLC2A1 (encodes GLUT1, predominant glucose transporter in granulosa cells), SLC2A4 (encodes GLUT4) and SLC2A10 (encodes GLUT10) between NEFA and BSA treated groups (Figure 2G). However, PDK4, a key regulator of pyruvate oxidation, was significantly upregulated by NEFA (Figure 2H). No significant changes were observed in the expression of receptors for follicle-stimulating hormone (FSH), insulin-like growth factor 1 (IGF1), or insulin (Figure 2I). Interestingly, the expression of STAR, a critical regulator of steroidogenesis, was significantly downregulated in NEFA treated cells (Figure 2J).

PDK4 silencing enhances glucose utilization

Transcriptome data revealed that PDK4 expression was significantly upregulated in the granulosa cells following NEFA supplementation. To determine the regulation of PDK4 by different NEFA species, we quantified its expression in granulosa cells treated with individual fatty acids (OA, PA, SA). We found that PDK4 expression was significantly induced by the saturated fatty acids PA and SA, but not by the unsaturated fatty acid OA (Figure 3A). This indicates that the induction of PDK4 expression in NEFA treated cells is primarily driven by PA and SA, thereby promoting fatty acid oxidation over the glucose via inhibiting PDC (Figure 3B). To assess the functional role of PDK4, we performed gene silencing using antisense GapmeR technology, achieving ~88% and ~95% reduction in PDK4 mRNA expression in BSA and NEFA treated cells, respectively (Figure 3C). The data showed a clear upregulation of PDK4 in response to NEFA treatment compared with BSA, consistent with the transcriptome results. Knockdown of PDK4 significantly upregulated the glucose transporter genes SLC2A1 and SLC2A10 (Figure 3D and E), and glycolytic enzymes GAPDH and ENO1 (Figure 3F and G), suggesting increased glucose uptake and glycolytic activities. Increased expression of LDHA (Figure 3H) further indicate that a portion of the imported glucose was directed toward aerobic glycolysis. Though these effects were observed in both BSA and NEFA treated cells, they were more pronounced in NEFA treated cells. Consistent with these transcriptional changes, glucose measurements in conditioned media showed that PDK4 knockdown increased glucose consumption, particularly under the NEFA treatment (Figure 3I). These findings suggest that PDK4 silencing may promote a metabolic shift toward glucose utilization, even in the presence of alternative energy substrates in the form of NEFA (OA, PA, and SA).

Figure 3.

PDK4 regulates glucose metabolism in granulosa cells. (A) mRNA expression of PDK4 upon individual fatty acid treatment in granulosa cells (n = 3). (B) Illustration depicting the induction of PDK4 expression by saturated fatty acids which eventually inhibit pyruvate oxidation thus the glucose oxidation. This graphic was generated at www.app.biorender.com. (C) mRNA expression of PDK4 in BSA and NEFA treated granulosa cells with or without PDK4 knockdown (n = 3). (D and E) mRNA expression of SLC2A1 and SLC2A10 in BSA and NEFA treated granulosa cells with or without PDK4 knockdown (n = 3). (F-H) mRNA expression of glycolytic genes GAPDH, ENO1, and LDHA in BSA and NEFA treated granulosa cells with or without PDK4 knockdown (n = 3). (I) glucose levels in the conditioned media of BSA and NEFA treated granulosa cells with or without PDK4 knockdown (n = 4). The individual data points of qPCR data are derived from the average of two or three cell replicates analyzed in each independent experiment. “Sham” represents cells treated with Sham GapmeR, “PDK4 Si” represents cells treated with antisense PDK4 GapmeRs. n = indicates the number of independent biological replicates analyzed. Data in (A) were analyzed by RM one way ANOVA with post hoc Tuckey test. All the remaining data were analyzed by two-way ANOVA to evaluate repeated measures with Tukey multiple comparison tests.

Effect on lipid accumulation and mitochondrial activity

NEFA supplementation induces lipid droplet accumulation in granulosa cells [15]. Consistent with this, present transcriptome data showed upregulation of the fatty acid transporters SLC27A1 and CD36 in NEFA treated cells (Figure 2F). We next examined effect of PDK4 silencing on SLC27A1 and CD36 expression and lipid accumulation in granulosa cells. Silencing of PDK4 resulted in downregulation of SLC27A1 (P = 0.05) and clear upregulation of CD36 under NEFA treatment compared to sham, whereas these effects were not significant in BSA treated cells (Figure 4A and B). Flow cytometry analysis confirmed that PDK4 silencing significantly increased lipid droplet accumulation in the presence of NEFA (Figure 4C and D).

Figure 4.

Effect of PDK4 silencing on fatty acid uptake and oxidation. (A and B) mRNA expression of SLC27A1 and CD36 in BSA and NEFA treated granulosa cells with or without PDK4 knockdown (n = 3). (C and D) Representative flow cytometry histograms of lipid droplet (LD) staining using Biotracker 488 and LD quantification in NEFA treated cells with or without PDK4 knockdown (n = 3). (E) mRNA expression of CPT1B in BSA and NEFA treated granulosa cells with or without PDK4 knockdown (n = 3). (F and G) Representative flow cytometry histograms of mitotracker staining and its quantification in NEFA treated cells with or without PDK4 knockdown (n = 3). (H) Quantification of cell numbers in BSA and NEFA treated granulosa cells with or without PDK4 knockdown (n = 4). (I and J) Flow cytometry dot plot of propidium iodide (PI) staining analysis in NEFA treated granulosa cells with Sham and PDK4 GapmeRs. -ve and + ve signs indicate PI negative (viable) and PI positive (dead) cells, respectively. The individual data points of qPCR data are derived from the average of two or three cell replicates analyzed in each independent experiment. “Sham” represents cells treated with Sham GapmeRs, “PDK4 Si” represents cells treated with antisense PDK4 GapmeRs. n = indicates the number of independent biological replicates analyzed. All the gene expression data were analyzed by two-way ANOVA to evaluate repeated measures with Tukey multiple comparison tests. The flow cytometry data were analyzed by paired t-test (two-tailed).

We then assessed CPT1B, a key gene mediating fatty acid transport into mitochondria for β-oxidation. CPT1B was significantly upregulated by NEFA treatment; however, PDK4 silencing led to a borderline decrease in its expression (P = 0.06) under NEFA treatment compared to sham (Figure 4E). Interestingly, MitoTracker staining revealed a significant increase in MMP in PDK4 silenced cells (Figure 4F and G). These findings suggest a potential shift in fatty acid trafficking away from oxidation and toward lipid storage in PDK4 silenced cells under elevated NEFA conditions. Cell counting analysis using Multisizer 3 Coulter Counter (Beckman) indicated increased cell number in NEFA treated cells compared to BSA. PDK4 silencing did not alter the cell count in BSA or NEFA treated cells (Figure 4H). Flow cytometry analysis indicated no significant changes in the cell viability upon PDK4 silencing in NEFA treated cells (Figure 4I and J).

PDK4 silencing decreases progesterone production

Luteal cells appear to favor fatty acid oxidation to meet their energy demands, whereas their predecessor granulosa cells of the ovarian follicle rely more heavily on glycolysis and are less dependent on fatty acid oxidation [26, 27]. We observed that PDK4 expression was markedly upregulated in both large and small luteal cells compared with granulosa and theca cells, respectively (Figure 5A). Similarly, CPT1A and CPT1B, key genes involved in mitochondrial fatty acid import, were significantly upregulated in large luteal cells relative to granulosa cells (Figure 5B and 5C). However, their expression was not significantly altered between theca and small luteal cells. These findings suggest that the differentiation of granulosa cells into luteal cells after ovulation is associated with a metabolic shift toward fatty acid oxidation, likely to support the increased energy demands and steroid hormone synthesis in luteal cells.

Figure 5.

PDK4 regulates progesterone production. (A–C) mRNA expression of PDK4, CPT1A, and CPT1B in granulosa cells (GC), large luteal cells (LLC), theca cells (TC), and small luteal cells (SLC). These expression values are derived from data mining of transcriptome data GSE83524. (D–F) mRNA expression of STAR, HSD3B, and CYP11A1 in BSA and NEFA-treated granulosa cells with or without PDK4 knockdown (n = 3). (G) Radioimmunoassay quantification of progesterone levels in the culture media. The individual data points of qPCR data are derived from the average of two or three replicates analyzed in each independent experiment. All the data were analyzed by two-way ANOVA to evaluate repeated measures with Tukey multiple comparison tests. n = indicates the number of independent biological replicates analyzed.

To explore the role of PDK4 in steroidogenesis, we analyzed the expression of key genes involved in progesterone biosynthesis such as STAR, HSD3B1, and CYP11A1 and measured progesterone production in PDK4-silenced granulosa cells. Results revealed downregulation of all three genes in PDK4 silenced cells compared to the sham (Figs 5D–F) in both BSA and NEFA treated cells. Consistent with these transcriptional changes, progesterone levels in the culture medium were significantly reduced (Figure 5G). Together, these results suggest that PDK4 acts not only as a metabolic regulator but also as a key factor in maintaining steroidogenesis.

Discussion

Cells reprogram their metabolic pathways in response to environmental changes through intrinsic regulatory mechanisms. Under metabolically challenging circumstances, such as fasting, hypoxia, obesity, and insulin resistance, cells could initiate metabolic shifts that could affect both glucose and fatty acid metabolism. In the present study, we demonstrate that supplementation of a NEFA mix in granulosa cells dramatically increases ATP levels and mitochondrial activity. This is particularly relevant as increased ATP levels can also be observed due to rapid glucose metabolism via aerobic glycolysis (Warburg effect) in granulosa cells [15, 28]. This is because NEFAs serve as substrates for β-oxidation in mitochondria and generate acetyl-CoA, which enters the TCA cycle and fuels oxidative phosphorylation and eventually the ATP synthesis. It has been demonstrated that NEFA exposure stimulates mitochondrial respiration, leading to higher ATP levels in follicular cells [7]. In the present experiments, we have used the combined NEFA concentration of 0.4 mM for cell culture supplementation, which did not affect the cell viability of granulosa cells. However, NEFA concentrations ≥1.2 mM have been shown to induce cell death due to excessive ROS production in bovine granulosa cells [29]. We acknowledge that NEFA concentrations in the follicular fluid are subject to dietary statuses or interventions. Starvation/NEB [4, 5], over-nutrition [5], and feed supplementation [30] have all been shown to influence the NEFA profile in follicular fluid of human, cattle, and other animals. The concentrations of OA, PA, and SA used in the present study were based on the measurements in the follicular fluid of NEB cattle [4].

Transcriptome wide analysis identified differential gene expression of 176 genes upon NEFA supplementation in bovine granulosa cells. Ingenuity pathway analysis (IPA) identified notable enrichments of biological processes linked to fatty acid metabolism, insulin sensitivity, steroidogenesis, and immune responses. Specifically, the upregulation of fatty acid oxidation genes such as CPT1A, CPT1B, HADHA, HADHB, and SLC25A20 aligns with the known role of NEFAs as substrates for mitochondrial β-oxidation, consistent with previous reports demonstrating increased fatty acid oxidation upon NEFA supplementation [7, 31, 32]. Conversely, the downregulation of key genes involved in de novo fatty acid synthesis, including ACACA and FASN, likely reflects a feedback mechanism triggered by elevated intracellular fatty acid concentrations, subsequently reducing lipogenesis to maintain metabolic homeostasis. In high-fat diet induced obese mice, Li and coworkers reported that increased fatty acid oxidation in the ovary during early pregnancy, along with an increase in ATP and lipids content [33]. Review articles have summarized that fasting induces plasma NEFA levels and drives fatty acid oxidation mediated by PPARα and CREB3L3 [34], and inhibition of acetyl-CoA carboxylase (ACACA) leading to an increase in CPT1 activity that promotes fatty acid oxidation in mitochondria [35]. Prolonged periods of starvation (day 2 and beyond) in humans resulted in a significant increase in NEFA levels and a corresponding surge in acylcarnitines and mitochondrial β-oxidation [36].

Current transcriptome data revealed increased expression of PDK4 in NEFA treated granulosa cells. Under conditions of stimulated fatty acid oxidation, PDK4 gene expression is increased via PPAR signaling [37], and elevated levels of citrate inhibit phosphofructokinase and glucose transporters, hindering glucose uptake, and utilization [38]. This regulation of fatty acids and glucose creates a substrate compensation and favors fatty acid oxidation under metabolic stress conditions, thereby preserving glucose for biosynthetic processes and other essential metabolic functions [35]. PDK4 is implied as a key enzyme involved in regulating the metabolic shift by preventing the oxidation of glucose [39]. In human peripheral blood mononuclear cells, the expression of PDK4 has been reported to be induced by elevated NEFA concentrations [40], as well as in fasting and starvation [17, 41]. However, whether it is caused by saturated or unsaturated fatty acids has not been well elucidated. In this study, the PDK4 expression is induced by NEFA supplementation in granulosa cells and is particularly activated by saturated fatty acids but not by unsaturated fatty acids. This observation suggests the cellular ability to distinguish different classes of fatty acids, triggering a specific metabolic reprogramming in response to the treatment.

We found that silencing PDK4 expression in NEFA treated cells increases the expression of glucose transporter and glycolytic genes and glucose consumption in granulosa cells. In line with the present data, PDK4 has been reported to regulate glucose uptake in neonatal myocytes in vitro as overexpression of PDK4 decreased and knockdown of PDK4 increased the glucose uptake. In the post ischemic myocardium, inhibition of PDK4 restored glycolysis and glucose oxidation in myocardium following reperfusion [42]. We found that silencing of PDK4 in NEFA treated granulosa cells resulted in upregulation of glucose transporters (SLC2A1 and SLC2A10), glycolytic genes (GAPDH, ENO1, and LDHA), and the fatty acid transporter CD36. In contrast, genes associated with fatty acid utilization, including SLC27A1 (a fatty acid transporter with additional acyl-CoA synthetase activity) and CPT1B, were downregulated. This suggests a restored capacity for glycolysis and glucose utilization with reduced reliance on the fatty acid oxidation, which is probably causing the increased CD36 expression and fat accumulation and in form of lipid droplets in the absence of PDK4. Moreover, the downregulation of SLC27A1 suggests that, in granulosa cells, this gene may play a predominant role in channeling fatty acids toward oxidation rather than storage. Normally, PDK4 inhibits PDC activity, reducing pyruvate conversion to acetyl-CoA and diverting metabolic dependency away from glucose toward fatty acids. Inhibition of PDK4 reactivates PDC, facilitating pyruvate entry into the TCA cycle and enhancing glucose oxidation [43, 44]. We speculate that, with glucose oxidation resumed, the cellular need for fatty acids diminishes in PDK silenced granulosa cells, prompting downregulation of fatty acid oxidation. A similar interpretation has been drawn in liver upon PDK4 inhibition in mice [45, 46]. Inhibition of PDK4 reactivated the PDC that could boost TCA cycle flux and electron transport chain activity, thereby increasing mitochondrial potential to improve energy efficacy [43]. Although we could not verify the PDK4 silencing at the protein levels due to unavailability of bovine reactive antibodies, the specific and clear regulation of various genes such as upregulation of glucose transporter and glycolytic genes, down regulation of fatty acid oxidation genes provides almost indisputable evidence of PDK4 gene knockdown at the protein levels under experimental conditions.

We show that PDK4 expression is dramatically induced in luteal cells compared to follicular cells, particularly granulosa cells. It has been reviewed that high levels of PDK4 leads to loss of metabolic flexibility in muscle, liver and immune system [47]. Taken together the dramatic upregulation of PDK4 in luteal cells suggests the dependency of corpus luteum on fatty acid oxidation for normal luteal functions. Our results have clearly showed that PDK4 knockdown significantly reduced progesterone production and downregulated corresponding gene expressions. This is the first reported link between PDK4 regulated metabolic changes and progesterone regulation in granulosa cells. Studies have shown that differentiation of granulosa cells into luteal cells is associated with increased formation of lipid droplets [48–50]. Several studies have shown that PDK4 regulates lipid metabolism and lipogenesis [51, 52]. A study in human THP-1 macrophages demonstrated that PDK4 appears in a regulatory feedback role involving miR-33, a microRNA known to regulate cholesterol homeostasis [53]. Inhibition of miR-33 (and thus potentially PDK4) promotes cholesterol efflux [54]. However, our results show that silencing of PDK4 downregulated the progesterone production, suggesting the need of further investigation in this regard. Przygrodzka et al. (2025) reported that inhibition of fatty acid oxidation using etomoxir in luteal cells inhibited progesterone production [26]. They suggested that fatty acid oxidation is required for sufficient mitochondrial energy production to power the progesterone biosynthesis in luteal cells. Based on the present data, we speculate that progesterone production of luteal cells is perhaps warranted by the dramatic upregulation of PDK4 compared to granulosa cells. However, further research is essential to understand whether and how increased glucose oxidation or decreased fatty acid oxidation observed in PDK4 silenced cells are mechanistically linked to downregulation of progesterone production (Figure 6).

Figure 6.

Schematic diagram summarizing the effects of PDK4 silencing in bovine granulosa cells supplemented with NEFA. Knockdown of PDK4 activates the pyruvate dehydrogenase complex, promoting cellular glucose uptake and oxidation. In parallel, PDK4 silencing suppresses fatty acid oxidation and progesterone production. This graphic was designed at www.app.biorender.com

Metabolic regulators are key modulators of granulosa cell function during follicular development. Particularly, insulin and IGF1 pathways are involved in follicle growth and selection as well as in steroidogenesis and proliferation of granulosa cells [55, 56]. Concentrations of IGF-1 in follicular fluid increases in large follicles compared to small follicles in mono-ovulatory species [57, 58]. Granulosa cells are also capable of producing IGF-1 as observed in porcine and ovine granulosa cells in vitro [59, 60]. In our transcriptome analysis, the expression of insulin and IGF1 receptors (INSR and IGF1R) was not altered by NEFA treatment, IGF1 expression remained very low and showed no significant changes. This suggests that elevated levels of NEFA may not disrupt these critical pathways of follicle development in granulosa cells. In the current study, granulosa cells were isolated from small antral follicles (<6 mm), minimizing heterogeneity associated with pooling follicles at different developmental stages and atretic follicles. Although this isolation method provides a degree of uniformity, it should be recognized that the isolation and in vitro culture of granulosa cells may alter the gene expression patterns relative to their in vivo state. This methodological consideration should therefore be taken into account when interpreting the data.

Overall, the present data reveal that PDK4 expression is induced in granulosa cells treated with NEFA, specifically by saturated fatty acids. Inhibition of PDK4 expression upregulates expression of glucose transporters and glycolysis, promoting glucose metabolism. In contrast, the expression of genes related to fatty acid oxidation was reduced. These results suggest a potential shift from fatty acid oxidation to glucose oxidation in NEFA treated granulosa cells upon knockdown of PDK4. Moreover, PDK4 was dramatically upregulated in luteal cells compared to granulosa cells. Inhibition of PDK4 expression downregulated the expression of progesterone biosynthetic genes and reduced progesterone production. We conclude that PDK4 plays a critical role in the metabolism, particularly metabolic flexibility, and is essential for optimum progesterone production in granulosa and luteal cells.

Author contributions

All the cell culture experiments were conducted and analyzed by XT. DK executed the transcriptome analysis of NEFA and BSA treated cells. XT and VSB analyzed the data and wrote the manuscript. VSB conceived the research idea and designed the experiments. JB performed IPA analysis, helped with glucose measurements and edited the manuscript. JV discussed experiments and data and edited the manuscript. All authors approved the present version of the manuscript.

Conflict of interest: The authors have declared that no conflict of interest exists.

Funding

Open Access funding enabled and organized by Projekt DEAL. This work is funded by Deutsche Forschungsgemeinschaft (DFG; grant No. BA 6909/1-1) to VSB. XT was funded by the China Scholarship Council (CSC) grant.

Data availability

The BSA vs NEFA transcriptome datasets generated in the present study can be found using the Gene Expression Omnibus accession number GSE302841. The other supporting data are available from the corresponding author, V.S.B., upon request.