{"paper_id":"643dacdb-eb5d-4606-87b4-9177fd5ded6f","body_text":"Diverse factors are held responsible for the worldwide increase in obesity rates in modern societies. Changes in dietary habits and reduced physical activity during the COVID-19 pandemic have also contributed to the recent rise in obesity among young people [ 1 ]. In women, obesity negatively affects fertility through complex mechanisms and is closely associated with an increased risk of many female reproductive conditions and fertility [ 2 , 3 , 4 ]. Polycystic ovary syndrome, reduced ovulation, and poor oocyte quality have been reported in women with obesity and in rodent models fed a high-fat diet (HFD) [ 5 , 6 , 7 ]. Uterine bleeding, endometriosis, preeclampsia, and miscarriage also increase in obese individuals [ 8 ]. Although the precise mechanisms underlying these issues have not been firmly established, it is evident that the ovarian environment adversely affects the reproductive health of women with obesity [ 4 ]. Owing to the aforementioned adverse effects of obesity on the reproductive health of women, the proportion of women with obesity seeking medical intervention to become pregnant is higher than that of women with normal weight [ 9 ].\nStudies on the effects of obesity on reproductive health have widely used rodent models fed HFD chow for specific periods [ 10 , 11 ]. The C57BL/6J mouse strain is widely used to investigate mechanisms underlying HFD-induced physiological and cellular changes in several systems [ 12 ]. When mice are fed HFD chow with a 60% fat content, they exhibit a range of physiological changes that mimic those occurring in obese humans [ 3 , 11 , 13 ]. A shorter period of HFD (~4 weeks) was shown to induce hyperglycemia and hyperinsulinemia in DIO mice, and prolonged HFD (~4 months) is accompanied by visceral fat accumulation, increased total fat mass, diabetes, and hypertension [ 12 ]. Prolonged HFD exposure (4 months) has been shown to impair ovulation, delay preimplantation embryo development, and cause post-implantation fetal growth defects [ 14 ], but whether short-term HFD influences the aspects of reproduction is not clear. Under HFD conditions, various organs and cells show changes in intracellular signaling pathways and increased apoptotic cell death [ 15 , 16 ]. Such molecular and cellular changes are collectively considered lipotoxic effects, as they result from excessive accumulation of fatty acids or lipid metabolites [ 15 ]. In various studies, HFD-fed mice showed obesity-induced changes in oocyte and follicular environments [ 7 , 17 , 18 , 19 , 20 , 21 ].\nGranulosa cells (GCs) surround growing oocytes within ovarian follicles and maintain close cell–cell communication during oocyte growth and ovulation [ 22 ]. These cells communicate with oocytes during oocyte development, follicle growth, ovulation, and hormonal regulation [ 23 ]. Therefore, exploring how GCs respond to a HFD environment is important to understand the changes that occur in oocytes and developing embryos after fertilization [ 24 , 25 ]. In this study, we investigated the changes in gene expression profiles in the GCs of HFD-fed mice to understand their effect on the ovarian environment. We report that  early growth response 1  ( Egr1 ) and other immediate early genes encoding transcription factors are suppressed in HFD GCs. We also found that immune cell-associated factors are affected by a HFD in GCs, providing novel insights into the molecular changes in the GC environment in obesity.\n\nAll the mice were maintained according to the guidelines of the International Animal Care and Use Committee (IACUC) of Konkuk University. The study was approved by Konkuk University IACUC (KU22157; approved on 22 Aug 2022 and KU23193; approved on 11 September 2023). Five-week-old female C57BL/6J mice ( n  = 100) were purchased from Raon Bio (Yongin-si, Republic of Korea). At the beginning of the experiments, the mice were fed regular chow for one week and then randomly divided into two groups. One group ( n  = 25) was fed a normal diet containing 10% fat (ND, D12450B, Research Diets, New Brunswick, NJ, USA), and the other group ( n  = 25) was fed a HFD containing 60% fat (HFD, D12492, Research Diets). The special chow feeding was initiated at 6 weeks of age and continued for 14–16 weeks in the long-term groups or for 4 weeks in the short-term groups ( Figure 1 A,B).\nTo collect blood samples for biochemical analyses, the mice were fasted for 12 h. Before drawing blood from the posterior vena cava the following morning, they were euthanized with Avertin (2,2,2-tribromoethanol in PBS, 250 mg/kg body weight, intraperitoneal injection). The first injection was given to put mice to sleep, and then, the second dose was given for euthanasia. During this process, all efforts were made to minimize suffering. Blood was drawn from the posterior vena cava and placed in a serum-separating tube. The blood samples were incubated for 30 min at 25 °C and then centrifuged at 850×  g  for 10 min at 25 °C. The supernatants were subjected to serum lipid analysis (total cholesterol [T-chol], glucose [GLU], triglyceride [TG], high-density lipoprotein [HDL], and low-density lipoprotein [LDL]). Data analysis was outsourced to the Korea Non-Clinical Technology Solution Center (Seongnam-si, Republic of Korea).\nThe body weights of all the animals were monitored weekly. To measure the fasting glucose levels, mice were fasted for 12 h before the test. A drop of blood was drawn from the tip of the tail, and the glucose level was monitored every four weeks using a glucometer.\nLiver and visceral adipose tissues were isolated from ND and HFD mice and fixed in 4% paraformaldehyde (PFA; Biosesang, Yongin-si, Republic of Korea) overnight. The tissues were then washed in PBS, dehydrated in serial dilutions of ethanol, and cleared in xylene. The tissues were embedded in paraffin, and sections were cut at a 10 μm thickness. The tissue sections were stained with hematoxylin–eosin (H&E) (Korea Non-Clinical Technology Solution Center). Slides were examined with an upright microscope (Nikon, Tokyo, Japan).\nOvarian GCs were isolated according to a protocol described by Campbell et al. [ 26 ]. Following the designated period of feeding, the ND or HFD mice were intraperitoneally injected with 10 IU of pregnant mare serum gonadotropin (PMSG; Daesung Microbiological Labs, Uiwang-si, Republic of Korea). The ovaries were isolated 48 h post-injection. To prepare the GCs, ovaries from four mice were pooled and subjected to GC isolation. Briefly, isolated ovaries were treated with 0.5 M ethylene glycol-bis-N,N,N′,N′-tetraacetic acid (EGTA; AG Scientific, San Diego, CA, USA) for 15 min, followed by incubation in hypertonic sucrose solution containing 0.5 M sucrose (Thermo Fisher Scientific, Waltham, MA, USA), 0.2% bovine serum albumin (BSA, Thermo Fisher Scientific), and 1.8 mM EGTA in Dulbecco’s Modified Eagle’s Medium/F12 (DMEM/F12, Thermo Fisher Scientific) for 15 min. The follicles were then punctured in DMEM/F12 containing 10% fetal bovine serum (FBS; Thermo Fisher Scientific) and 1% penicillin–streptomycin (Thermo Fisher Scientific) to obtain GCs.\nMice fed ND or HFD for 16 weeks (20 per group) were used for GC isolation. Total RNA was isolated from pooled samples consisting of GCs isolated from four mice (ND- or HFD-fed mice). Total RNA was extracted using TRIzol ®  Reagent (Invitrogen, Carlsbad, CA, USA). Briefly, the tissues were homogenized in TRIzol (500 μL for the ovary and 300 μL for the pooled GCs), followed by the addition of one-fifth the volume of chloroform. After thorough mixing and incubation at 25 °C for 15 min, the samples were centrifuged at 4 °C for 20 min. The aqueous phase was transferred to a fresh tube, and one-tenth the volume of 3 M sodium acetate and an equal volume of isopropanol were added. After vortexing and centrifugation at 15,956×  g , the resulting RNA pellet was washed twice with 80% ethanol, air-dried, and resuspended in 20 μL of nuclease-free water. The RNA was treated with DNase (Promega, Madison, WI, USA) for 20 min at room temperature to denature the DNA, and the DNase was inactivated by incubation at 55 °C for 10 min. The RNA concentration and quality were assessed using the spectrophotometer (NanoDrop ®  One; Thermo Fisher Scientific, Waltham, MA, USA). Complementary DNA (cDNA) was synthesized from 2 μg of RNA using random hexamer primers (Invitrogen), oligo (dT) primers, M-MLV reverse transcriptase (Promega), and RNase inhibitor (Promega). To quantify the gene expression levels, reverse transcription-quantitative PCR (RT-qPCR) was performed with SYBR ®  green dye (Bio-Rad Laboratories, Hercules, CA, USA) using 2 μL of cDNA in a final volume of 20 μL. A CFX Duet Real-Time PCR system (Bio-Rad Laboratories) was used for the analysis. Gene expression was normalized to that of the housekeeping gene  ribosomal protein L7  ( Rpl7 ). The primer sequences used in this study are listed in  Table 1 . GraphPad Prism software (version 5; GraphPad Software Inc., La Jolla, CA, USA) was used to construct barograms and perform Student’s  t -tests. One-way analysis of variance and Tukey’s honestly significant difference test were performed using ChatGPT-4 (OpenAI, San Francisco, CA, USA).\nMice fed ND or HFD for 16 weeks (20 per group) were used for GC isolation. Total RNA was isolated from pooled samples consisting of GCs isolated from four mice (ND- or HFD-fed mice). RNA sequencing was performed by LAS (Gimpo, Republic of Korea). RNA quality was assessed using the Agilent TapeStation 4000 System (Agilent Technologies, Amstelveen, the Netherlands). RNA was quantified using a ND-2000 spectrophotometer (Thermo Fisher Scientific). RNA libraries were constructed using a QuantSeq 3 mRNA Seq Library Prep Kit (Lexogen Inc., Vienna, Austria) according to the manufacturer’s instructions. High-throughput 75 single-end cycle sequencing was performed using NextSeq 550 (Illumina Inc., San Diego, CA, USA). The quality of the reads was examined using fast QC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ , accessed 19 December 2016), and low-quality (Phred quality score < 20) reads were trimmed using BBDuk, a part of bbmap software ( http://sourceforge.net/projects/bbmap/ , accessed 2 September 2019). After removing low-quality reads, the sequencing reads were aligned by Bowtie2 [ 27 ] using the reference genome of the Illumina iGenomes UCSC mm10. Reads were quantified using the genes.gtf gene annotation file from the Illumina iGenomes UCSC mm10 with BedTools [ 28 ]. After excluding 1199 microRNAs, 23,282 protein-coding mRNAs were analyzed.\nFrom a total of 23,282 genes in the raw counts, 6852 low-expression genes (with no more than 10 reads in at least five samples) were excluded, leaving 16,430 genes for subsequent analyses. Differential expression (DE) analysis of the transcriptome data was conducted using DESeq2 1.44.0 [ 29 ]. For identifying DE genes (DEGs) between ND and HFD mice, we employed DESeq2  p  < 0.01 and |log 2  fold change| ≥ 0.5 as the criteria. A false discovery rate was applied to adjust the  p -values. The top DEGs with >1.5-fold change ( p  < 0.05) are represented in the heatmap. Raw data for RNA-seq are deposited in the Korea Bio Data Station ( https://kbds.re.kr , accessed on 3 December 2024, accession number KAP241039).\nFor principal component analysis (PCA), the variance-stabilizing transformation in DESeq2 [ 29 ] was applied to the raw counts. PCA was performed using the R statistical package. For the hierarchical clustering analysis, we first normalized the raw counts according to the library size of each sample using the DESeq2 package and transformed them into a log2 scale. Pearson’s correlation coefficient was used to calculate the distance between samples, and a complete linkage option was chosen. A hierarchical clustering analysis was performed using the R statistics package. PCA and volcano plots were obtained using the gplot2 R package [ 30 ]. Heatmaps were drawn using the Complex Heatmap package in R. All data were analyzed using R version 4.3.0.\nFor Western blotting, each sample was prepared from a single ovary. The collected ovary was homogenized in RIPA buffer [10 mM Tris (pH 7.2; HanLAB, Cheongju-si, Republic of Korea), 150 mM NaCl (Thermo Fisher Scientific), 0.1% Triton X-100 (Sigma-Aldrich, St. Louis, MO, USA), 5 mM EDTA (HanLAB), 1% SDS (Bio-Rad), 1 mM DTT (Sigma-Aldrich), 1 mM PMSF (MP Biomedicals, Irvine, CA, USA), and a protease inhibitor cocktail (Roche, Basel, Switzerland)]. After homogenization, the samples were centrifuged at 15,928×  g  for 20 min at 4 °C. Protein concentrations were measured using the BCA protein assay kit (Thermo Fisher Scientific). The extracted proteins were separated on a 10% SDS–polyacrylamide gel and subsequently transferred onto nitrocellulose membranes (Bio-Rad). Detection of chemiluminescent signals was performed using the WestFemto kit (Thermo Fisher Scientific). The protein expression levels were normalized to β-tubulin (TUBB) or glyceraldehyde 3-phosphate dehydrogenase (GAPDH) levels and quantified with NIH ImageJ software v1.54g. Data are presented as the mean ± standard error of the mean (SEM). All Western blotting experiments were repeated at least three times, and one representative set is shown. Details of the primary antibodies used are provided in  Table 2 .\nThe isolated GCs were seeded on glass coverslips and placed in a 12-well plate at a density of 2 × 10 5  cells/coverslip. GCs were fixed in 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS; Gibco, Thermo Fisher Scientific) for 20 min and washed thrice with PBS for 5 min each. Cells were permeabilized with 0.25% Triton X-100 (Sigma-Aldrich) for 10 min. To prevent non-specific binding, the cells were treated with 2% BSA in PBS for 1 h at room temperature. The cells were then incubated with primary antibodies ( Table 2 ) at 4 °C overnight. After washing, the cells were incubated with secondary antibodies for 1 h at room temperature in the dark. The DNA was counterstained with TO-PRO-3-iodide (Invitrogen). Coverslips were mounted on glass slides using the Vectashield mounting medium (Vector Laboratories, Newark, CA, USA) and observed under a Zeiss LMS900 confocal microscope (Carl Zeiss AG, Oberkochen, Germany). The images were obtained and analyzed using ZEN Blue software version 3.4 (Carl Zeiss AG).\nParaffin-embedded ovarian tissue sections were deparaffinized in xylene and rehydrated in serial dilutions of ethanol and PBS. The slides were subjected to antigen retrieval using an Antigen Unmasking Solution (Vector Laboratories). To obtain cryosections, sucrose-embedded ovaries were frozen, cut into 12 μm sections, and fixed with 4% PFA for 20 min. The cryosections were then treated with 0.25% Triton X-100 for 10 min and blocked with 2% BSA. The sections were then incubated with the primary antibody at 4 °C overnight. The following day, the sections were incubated with secondary antibodies at room temperature for 1 h in the dark. All experiments accompanied the negative control stained with the corresponding IgG as the primary antibody. The negative control experiments did not generate any signal. The antibodies used are listed in  Table 2 .\n\nTo screen genes influenced by a HFD in GCs, we used a widely used model of diet-induced obesity in C57BL/6J mice [ 31 , 32 , 33 , 34 ]. Mice were randomly divided into two groups (25 mice per group), and each group was either fed ND or HFD for a specified period ( Figure 1 ). As reported previously by investigations using similar models, HFD mice showed expected serological and histological changes ( Figure 1 ). HFD mice showed a significant increase in body weight starting in the second week compared to ND mice. At the end of the experimental period, the HFD mice were approximately 70% heavier than the ND mice ( Figure 1 C). The fasting blood glucose levels (measured once a month) were significantly higher in HFD mice than those of ND mice at all measurement points ( Figure 1 C). By the 17th week, the HFD group demonstrated an approximately 34% increase in blood glucose levels compared to the ND group. At the experimental endpoint, the mice were fasted for 12 h, and blood samples were collected for lipid and glucose measurements. As shown in  Figure 1 D, significant increases in the levels of T-Chol, GLU, and HDL were confirmed in HFD mice, whereas the LDL and TG levels were not substantially different between the two groups. These results indicate that a prolonged HFD alters the physiological metabolic parameters, consistent with the results observed in other investigations [ 31 , 32 , 33 , 34 ]. As reported previously [ 31 ], the liver and visceral adipose tissue sections from HFD mice showed an increase in the volume of lipid droplets ( Figure 1 E, black arrows).\nWe performed RNA-seq analysis to investigate the transcriptomic changes in GCs isolated from ND and HFD mice. In the principal component analysis (PCA), the first two principal components (PC1 and PC2), which had the highest explanatory power for the transcriptome, were not significantly different between the ND and HFD groups ( Figure S1 ). The HFD and ND groups were well separated, according to the third principal component (PC3), as shown in  Figure 2 A. To identify the genes associated with PC3, we performed a differentially expressed gene (DEG) analysis. A total of 151 DEGs were identified between the ND and HFD groups. Of these genes, 43 were upregulated and 108 were downregulated in HFD mice compared to ND mice ( Figure 2 B). A PCA plot based on these DEGs also clearly separated HFD mice and ND mice according to PC1 ( Figure 2 C). A few representative DEGs are shown in  Figure 2 D. Several gene ontology (GO) pathways were prominent among the downregulated DEGs. A list of DEGs and GO terms is provided in  Table S1 (Supplementary Materials) .\nA heatmap of the top 21 downregulated DEGs in the HFD GCs ( Figure 3 A) revealed the downregulation of several immediate early genes encoding transcription factors, including  activating transcription factor 3  ( Atf3 ),  FBJ osteosarcoma oncogene  ( Fos ),  FBJ osteosarcoma oncogene B  ( Fosb ), and  Jun B proto-oncogene  ( Junb ) belonging to the activator protein-1 (AP-1) family of transcription factors that bind to specific regulatory regions to modulate gene expression [ 35 ].  Early growth response 1  ( Egr1 ), also an immediate early gene that contains a zinc-finger DNA-binding domain [ 36 ], is indispensable for ovulation in mice and is known to be induced by gonadotropins [ 37 , 38 ]. In the ovary, FOS acts as a critical downstream mediator of the progesterone receptor and epidermal growth factor signaling and increases the expression of genes important for ovulation [ 39 ]. Thus,  Egr1  and  Fos  were selected for further evaluation due to their involvement in ovarian functions [ 37 , 38 , 39 ].\nWe first performed RT-qPCR analysis using separate sample sets of GCs to confirm that the  Egr1 ,  Fos , and  Fosb  mRNA levels were significantly downregulated in the HFD GCs ( Figure 3 B). Immunofluorescence staining of FOS in ovarian sections and isolated GCs showed that it colocalized with FOXL2, a GC marker [ 40 ] ( Figure 3 C,D).\nWe next examined if an acute diet change, such as shorter-term HFD, similarly affects gene expression in the ovary. As the RNA-seq data were obtained from GCs of mice following 14–16 weeks of a HFD, we sought to investigate whether a similar change in gene expression occurs in GCs of mice with 4 weeks of a HFD ( Figure 1 B, four weeks of a HFD). As shown in  Figure 3 E, the expression of  Egr1  and  Fos  was downregulated, consistent with the results from the long-term HFD group, suggesting that HFD induces early changes in gene expression.\nEGR1 is indispensable for ovulation in mice and is known to be induced by gonadotropins [ 37 , 38 ]. Specifically, EGR1 was previously shown to be induced at its highest level during the early hours of hCG induction [ 38 ]. We first confirmed this previous observation that the EGR1 levels fluctuated during gonadotropin administration, with the highest levels observed at 3 h post-hCG induction ( Figure 4 A,B). As our RNA-seq data were obtained from GCs at 48 h post-PMSG injection, we compared the expression of  Egr1  in HFD mice at two different time points: PMSG 48 h, at which our analysis was performed, and PMSG + hCG 3 h, at which  Egr1  showed the highest induction. The EGR1 levels were significantly lower in the HFD ovary at 48 h after PMSG injection, whereas, at 3 h, the levels were similar between the ND and HFD groups. These results confirmed that EGR1 was suppressed in HFD GCs at 48 h post-PMSG.\nAmong the upregulated DEGs in HFD GCs ( Figure 5 A),  protein tyrosine phosphatase receptor type C  ( Ptprc ) and  hematopoietic prostaglandin D synthase  ( Hpgds ) were notable, as they are associated with increased inflammation.  Ptprc  encodes the cluster of differentiation 45 (CD45), a pan-hematopoietic cell marker and regulator of immune cell function [ 41 ], whereas HPGDS catalyzes the conversion of prostaglandin H 2  into prostaglandin D 2  (PGD 2 ) in hematopoietic cells and other tissues [ 42 , 43 ]. As obesity is associated with increased inflammation in various tissues, including the ovary [ 44 , 45 ], the expression and localization of CD45 and HPGDS were further evaluated in HFD ovaries.\nAs shown in  Figure 5 B, the  Hpgds  mRNA level was comparable in the GCs from ND and HFD mice. Immunofluorescence staining of the isolated GCs showed positive HPGDS signals, along with overlapping FOXL2, suggesting that these cells may produce PGD 2  and influence the surrounding cellular environment ( Figure 5 C). In the ovarian cryosection, HPGDS was observed within the follicle, as well as in the stromal region in the HFD ovaries. Oocytes also showed positive signals ( Figure 5 D). Thus, HPGDS is localized both within and outside ovarian follicles. Since PGD 2  produced by HPGDS is implicated in the increasing tissue infiltration of leukocytes, we used  Ptprc -encoded CD45, a pan-leukocyte marker, to examine if there was an increased immune cell population within the HFD ovaries. As shown in  Figure 6 A, CD45-positive cells were present at various locations in both the ND and HFD ovaries.\nAmong the upregulated DEGs,  Ptprc  and  Hpgds  have been implicated in increased inflammation [ 46 , 47 , 48 , 49 ]. As prolonged HFD may increase the infiltration of immune cells in the ovarian environment, we examined whether there was enhanced immune cell recruitment within the ovarian environment by evaluating F4/80- (a pan-macrophage marker) and CD68 (an activated-macrophage marker)-positive cells [ 50 , 51 , 52 ]. The combined use of F4/80 and CD68 markers is a common approach to identify and characterize macrophages in the ovarian tissue of mice [ 53 ].  Figure 6 B shows that F4/80- and CD68-positive cells were distributed in the stromal regions of HFD ovaries.\n\nThis study aimed to investigate the molecular and cellular changes in the ovarian environment of mice with diet-induced obesity. Various studies on the effects of diet on ovarian function have revealed its effects on oocyte quality, changes in follicular fluid, and pregnancy outcomes [ 54 , 55 , 56 ]. In this study, we examined the effects of a HFD on gene expression changes in isolated GCs of mice and identified significant alterations in the expression of several key genes involved in reproductive functions. As immediate early response transcription factors, EGR1 and members of the AP-1 family regulate early responses to growth stimuli and engage in a wide range of transcriptional regulatory processes associated with cellular proliferation, survival, inflammation, and differentiation [ 35 , 57 , 58 ]. As mentioned, FOS is known as a downstream mediator of the progesterone receptor and epidermal growth factor signaling in the ovary [ 39 ]. Thus, the downregulation of  Fos  implies that HFD may compromise ovulatory function in mice. EGR1 plays a crucial role in mediating luteinizing hormone responses necessary for steroidogenesis and ovulation [ 37 , 38 ] and the actions of steroid hormones in the uterus [ 59 , 60 ]. Thus, the reduced expression of these transcription factors in HFD GCs suggests that diet-induced metabolic stress may impair the ability of GCs to respond effectively to gonadotropins, potentially leading to the ovulatory dysfunction and subfertility observed in obesity. Among these factors, the association between  Egr1  and obesity has been documented in other systems, suggesting a certain level of complexity. In adipocytes,  Egr1  is induced by HFD and is linked to increased energy storage in white adipose tissue [ 61 ].  Egr1  is induced in GCs isolated from obese pregnant rats during the preimplantation period [ 62 ]. As  Egr1 -deficient mice exhibit partial protection against fat deposition under HFD conditions [ 63 ], the possibility that EGR1 is a target of metabolic disorders is further strengthened by our findings. As for the human ovary, EGR1 is implicated in increasing the incidence of ovarian hyperstimulation syndrome [ 64 ], but whether ovarian EGR1 is associated with women’s body weight is unknown.\nObesity increases inflammation at a systemic level [ 65 , 66 ]. An increased infiltration of the immune cells has been reported in adipose tissue and the liver [ 67 , 68 ]. CD45, encoded by  Ptprc,  is expressed in almost all nucleated hematopoietic cells in mice [ 69 ] and is a key regulator of immune cell signaling [ 41 ]. CD45 has been implicated in the modulation of the local immune environment in the ovaries of patients with polycystic ovarian syndrome (PCOS) [ 48 ]. The significantly elevated  Ptprc  levels in HFD GC preparations in the present study may indicate the presence of more immune cells in HFD ovaries, accompanied by increased inflammation. Modulations in the immune factors in HFD ovaries may reflect an alteration in the inflammatory response or immune surveillance mechanisms within the ovarian microenvironment. Perturbation in the immunological environment can interfere with normal follicular development and ovulation. Similarly, the upregulation of  Hpgds , an enzyme involved in prostaglandin D 2  synthesis, suggests an increased production of PGD 2 , which, in turn, may enhance inflammatory or stress responses in the ovary. The expression of HPGDS has been reported in GCs [ 42 ] and in immune and mast cells during inflammatory responses [ 70 ]. Thus, the source of PGD 2  that attracts immune cells may be the GCs or immune cells in the ovary.\nThe downregulation of  Egr1  and  Fos  was also observed in GCs from short-term HFD mice, which were under a HFD for 4 weeks. This observation is particularly important in the field of reproductive biology, as a prolonged period of special diet renders them older at the experimental endpoint, when their reproductive performance may be less robust. If a short-term diet change also produces similar effects, experimental designs become far less complex and can be performed with mice at younger ages. In this context, it is notable that one week of a HFD was reported to be sufficient to cause calcium signaling in the liver [ 71 ].\nOur study provides novel insights into the early molecular changes in GCs induced by a HFD. We demonstrate, for the first time, that EGR1, a crucial transcription factor for ovulation, is significantly downregulated in response to a HFD in the ovary, even after short-term exposure. This suggests that metabolic stress may impair ovulatory function via EGR1 downregulation. Furthermore, we identify an increased expression of  Ptprc  and  Hpgds  in GCs, highlighting the implication of granulosa cells in modulating the ovarian inflammatory environment in obesity. Our findings also reveal that key transcriptional changes occur after only four weeks of HFD, underscoring the rapid impact of dietary composition on ovarian function and the potential for early intervention to mitigate obesity-associated reproductive dysfunction. Collectively, our findings highlight the complex interplay between the metabolic state, immune regulation, and reproductive function in GCs. Understanding the molecular changes in GCs in diet-induced obesity models could potentially lead to the development of targeted interventions or biomarkers for obesity-related ovarian malfunction. These basic findings may ultimately improve the diagnostic and treatment strategies for women struggling with obesity-associated infertility. Our results suggest that HFD-related metabolic dysregulation significantly alters the transcriptional landscape in GCs, potentially disrupting important pathways involved in ovarian function and fertility. Our results also provide insights into the mechanisms underlying the reduced efficacy of assisted-reproductive technologies in women with obesity. Further investigation is warranted to identify whether similar mechanisms operate in human GCs.","source_license":"CC-BY-4.0","license_restricted":false}