Serum Proteomic and Metabolomic Profiling Uncovers Molecular Mechanisms in Patients Undergoing In-Vitro Fertilization with the GnRH Antagonist Protocol

Serum Proteomic and Metabolomic Profiling Uncovers Molecular Mechanisms in Patients Undergoing In-Vitro Fertilization with the GnRH Antagonist Protocol | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Serum Proteomic and Metabolomic Profiling Uncovers Molecular Mechanisms in Patients Undergoing In-Vitro Fertilization with the GnRH Antagonist Protocol Tian-Min Ye, Hongfang Shao, Na Wang, Minfang Tao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7779262/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives Infertility is a global health challenge commonly treated with assisted reproductive technology (ART), particularly gonadotropin-releasing hormone (GnRH) antagonist protocols. However, the molecular mechanisms remain unclear. This study aimed to analyze serum proteomic and metabolomic profiles during the menstrual cycle (M) and ovum pick up day (OPU) in patients under this protocol. Design: Ten patients undergoing the GnRH antagonist protocol were enrolled. Serum samples collected during the M and OPU phases were used for proteomics and non-targeted metabolomics analyses. Functional enrichment, protein-protein interaction (PPI), and correlation analyses were performed. Results Clinical indicators confirmed the efficacy of the GnRH antagonist protocol, as evidenced by an anti-Müllerian hormone level of 3.70 ng/ml, significantly elevated sex hormone levels at OPU, and 15.3 oocytes and 5.3 embryos retrieved. Proteomics identified 83 differentially expressed proteins enriched in cell cycle, AMPK, oocyte meiosis, and PI3K-Akt pathways, with core regulatory proteins including RPL23, TCP1, and PRDX1. Metabolomics revealed 66 differential metabolites involved in steroid hormone biosynthesis, glutathione, and purine metabolism pathways. Integrated analysis obtained 192 significantly correlated protein-metabolite pairs, emphasizing the glutathione, purine metabolism, and nucleotide sugar biosynthesis pathways. Key relationships such as NUDT16-inosine and GSR-glutathione were also identified. Conclusion This study firstly revealed the dynamic changes in the serum proteome and metabolome between the M and OPU phases under the GnRH antagonist protocol. The study highlighted the role in regulating hormone synthesis, oxidative balance, and energy supply to support follicular development. These findings provide new insights and potential biomarkers for improving IVF outcomes. Biological sciences/Biochemistry Health sciences/Biomarkers Health sciences/Endocrinology Biological sciences/Molecular biology GnRH antagonist protocol proteomics metabolomics follicular development in-vitro fertilization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Infertility is a global health issue which affects more than millions of couples worldwide. According to global estimations, the condition of infertility affected a substantial number of individuals, reaching an approximate 48.5 million people globally 1 . In China, the prevalence of infertility was 25% among couples of reproductive ages 2 . The application of assisted reproductive technology (ART) has become increasingly common for treating of infertility. With the development of ART, it is necessary for the doctors to choose an ovarian stimulation protocol that is safe and efficient for the patients. Nowadays, the gonadotropin-releasing hormone (GnRH) antagonist protocol has become widely used in clinical ART practice 3 . The advantages of the GnRH antagonist protocol include short duration of administration, low cost, flexible regimen, rapid pituitary recovery, and no "flare-up" effect 4 . The GnRH antagonist protocol not only effectively suppresses spontaneous early luteinizing hormone (LH) surge and increases the number of oocytes retrieved but also significantly reduces the occurrence of ovarian hyperstimulation syndrome (OHSS) 5 . In recent years, some studies have reported that there was no significant difference between live birth and ongoing pregnancy rates between optimized GnRH agonist and antagonist protocols, which has contributed to its growing clinical application 6 . Although the clinical value of the GnRH antagonist protocol has been recognized, there remains a significant gap in the understanding of the dynamic molecular changes occurring in patients during its therapy. Usually, higher serum steroid hormone levels than those of a those of a natural menstrual cycle are detected during controlled ovarian stimulation 7 . The protein expression in serum fluctuates after the treatment, and it should reveal the mechanism underlying physiological responses to ovarian stimulation, such as OHSS 8 . However, current research on the specific changes in proteins and metabolites in patients and their potential impacts during GnRH antagonist treatment remains limited. Most existing literature focuses on a single biomolecular level, lacking integrated proteomic and metabolomic analysis, making it difficult to fully reveal the molecular network regulatory mechanism underlying drug intervention 9 , 10 . Furthermore, most studies only adopt static detection methods and fail to dynamically capture the temporal variation characteristics of metabolic pathways 10 , 11 . Although some studies have focused on changes in physiological indicators during in-vitro fertilization (IVF) treatment, systematic research on the proteomic and metabolomic characteristics at two key time points, the menstrual cycle and ovum pick up day, in patients receiving GnRH antagonist treatment remains relatively insufficient 12 . These two time points not only correspond to the period of drastic hormonal fluctuations in the female reproductive cycle, but also serve as key nodes for the GnRH antagonist to exert its clinical effects. The lack of understanding of their molecular characteristics makes it impossible to clarify the specific impact of the drug on physiological processes such as neuroendocrine regulation and energy metabolism remodeling, which severely limits the in-depth understanding of the therapeutic mechanism of this protocol and the optimization of individualized protocols 13 . Therefore, this study aimed to collect serum samples from IVF patients undergoing GnRH antagonist treatment at M and OPU phases, and conducted integrated proteomic and metabolomic analyses. By exploring the dynamic variation patterns of proteins and metabolites in patients, this study sought to provide important theoretical support and experimental evidence for elucidating the molecular mechanism by which GnRH antagonists regulated the hypothalamic-pituitary-ovarian axis, predicting patients’ ovarian responsiveness, and improving the clinical pregnancy rate of IVF. Material and methods 2.1 Patient inclusion criteria This study recruited 10 women who received the GnRH antagonist protocol in the Reproductive Medicine Center of Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine. Inclusion criteria included undergoing GnRH antagonist IVF/ICSI (Intra-cytoplasmic sperm injection) protocol; first fresh stimulation cycle; male factor infertility in the couple; complete clinical and follow-up data were available during the treatment cycle; and no prior history of IVF treatment. Exclusion criteria included the presence of malignant tumors, abnormal immune function, recent use of medications affecting protein and metabolism, history of mental illness, and any other conditions not meeting the above inclusion criteria. This study has been approved by the ethics committee of Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine, and all research subjects have signed informed consent forms. All procedures involving human participants were performed in accordance with the Declaration of Helsinki. 2.2 GnRH antagonist protocol for ovulation induction All participants underwent ovulation induction therapy using a GnRH antagonist protocol. Gonadotropin (Gn) injections were administered subcutaneously starting on day 2 of the menstrual cycle, with concurrent monitoring of serum levels of follicle stimulating hormone (FSH), estradiol (E), progesterone (P), and LH. A transvaginal ultrasound was performed to assess antral follicle count (AFC). The initial dose of recombinant FSH (r-FSH) was adjusted based on the AFC results. If AFC ≤ 6, 300 IU was administered to initiate superovulation; if 6 < AFC < 15, 225 IU was administered; if AFC ≥ 15, 150 IU was administered. The r-FSH was chosen from Gonal-f (Merck Serono, Germany) or Puregon (Merck Sharp & Dohme, USA). As a fixed antagonist protocol, subcutaneous injections of Ganirelix acetate (0.25 mg/day, Merck Sharp & Dohme, USA) were initiated on day 6 of r-FSH administration. When the average diameter of one dominant follicle was ≥ 18 mm, and the average diameter of the other two dominant follicles was ≥ 16 mm, the administration of gonadotropin (Gn) was stopped, and human chorionic gonadotropin (hCG) was administered, followed by oocyte retrieval 36 hours later. Blood samples were collected both on day 2 of the menstrual cycle (M) and on the day of ovum pick up (OPU). After blood collection, serum samples were separated for subsequent testing. 2.3 Protein extraction and liquid chromatography-mass spectrometry (LC-MS) analysis An appropriate amount of serum samples was mixed with 50 mM ammonium bicarbonate buffer, and the protein was inactivated at 95°C for 3 min. After cooling, trypsin was added at an enzyme-to-protein mass ratio of 1: 25, and digestion was performed at 37°C for 16 h. Subsequently, the peptides were extracted and dried. Finally, the peptides were reconstituted with 100 µL of 0.1% formic acid solution and separated and identified on an SDS-PAGE gel. The samples were separated using a high-performance liquid chromatography system (EASY-nLC 1200, Thermo Fisher Scientific, USA) with a nanoliter flow rate. Mobile phase A was a 0.1% formic acid aqueous solution, and mobile phase B was a 0.1% formic acid acetonitrile aqueous solution containing 80% acetonitrile. First, the injection column and analytical column were equilibrated with 100% mobile phase A. Subsequently, the enzyme-digested peptide segments of the samples were separated using C18 injection columns (injection column: 2 cm in length, 100 µm inner diameter, 3 µm particle size; analytical column: 15 cm in length, 150 µm inner diameter, 1.9 µm particle size) at a flow rate of 600 nL/min. Mass spectrometry analysis was performed using a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA) with an electrospray ionization (ESI) source in positive ion mode. The parent ion scanning range was 300–1400 m/z, with a primary mass spectrometry resolution of 60,000 at 200 m/z, an automatic gain control (AGC) target value of 3e6, and a maximum injection time (IT) of 20 ms. For the secondary mass spectrometry, data-independent acquisition (DIA) mode was adopted, with 20 DIA scans collected after each full scan. The higher-energy collisional dissociation (HCD) fragmentation mode was used with a normalized collision energy of 27%. The isolation window varied according to the isolation window settings, and the secondary mass spectrometry resolution was 15,000 at 200 m/z. 2.4 Protein identification and analysis Raw data were analyzed using the iProteome data analysis cloud platform for database-based qualitative and quantitative analysis 14 . The database search parameters were set as follows: Enzyme: Trypsin; Database: Custom library; Fixed modifications: Carbamidomethyl (C); Variable modifications: Oxidation (M) and Acetyl (Protein N-term); Missed Cleavage: 2; Peptide Mass Tolerance: 20 ppm; and Fragment Mass Tolerance:0.05 Da. The peptide false discovery rate ≤ 0.05 was used as the screening criterion. After completing the database search, the peptide peak areas were normalized using z-scores and summarized as protein quantification values. The quantification data were processed to remove contaminant proteins, and impute missing values. The criteria for identifying differentially expressed proteins were a fold change ≥ 1.5 and a p -value < 0.05 after Benjamini-Hochberg correction. Protein domain features were identified from the Pfam database ( https://pfam.xfam.org/ ) using InterProScan 15 . Homologous sequences of differentially expressed proteins were searched via NCBI BLAST and InterProScan, followed by mapping gene ontology (GO) terms to complete the annotation using Blast2GO 16 , 17 . Kyoto encyclopedia of genes and genomes (KEGG) annotation was obtained by comparing with the KEGG database and mapped to corresponding pathways. Differentially expressed proteins were then subjected to GO functional (including molecular function, cellular component, and biological process) and KEGG pathway enrichment analysis. Protein interaction networks were constructed based on the STRING database ( http://string-db.org/ ) and visualized and analyzed using Cytoscape 18 . 2.3 LC/MS non-targeted metabolomics analysis An appropriate amount of sample was taken, and a pre-cooled methanol-acetonitrile solution (1:1) was added. After vortex mixing for 30 s, the mixtures were incubated at -20°C for 1 h. They were then centrifuged at 14,000 g for 20 min at 4°C, and the supernatant was transferred to a new tube. Meanwhile, a mixture of samples was taken to prepare quality control (QC) samples. Chromatographic separation was performed using a Nexera UHPLC LC-30A system (Shimadzu, Japan) equipped with a HILIC column (Waters, ACQUITY UPLC BEH Amide, 1.7 µm particle size, 2.1×100 mm). The column was equilibrated with 98% mobile phase A, and the sample was loaded with a flow rate maintained at 0.3 mL/min. The elution program was as follows: initial 2% mobile phase B maintained for 0.5 min, followed by 11.5 min increase to 98% mobile phase B, held for 4 min, then returned to 2% mobile phase B within 0.1 min, and washed for 1.9 min. Among them, in the positive ion mode (POS), mobile phase A was 10 mM ammonium acetate-acetonitrile water (95:5, containing 0.1% formic acid); mobile phase B was 10 mM ammonium acetate-acetonitrile water (50:50, containing 0.1% formic acid). In the negative ion mode (NEG), mobile phase A and B were 10 mM ammonium acetate-acetonitrile water (95:5) and 10 mM ammonium acetate-acetonitrile water (50:50), respectively, both adjusted to pH 8.0 with ammonia water. The samples after chromatographic separation were analyzed by a Q Exactive HF-X mass spectrometer (Thermo Fisher, USA), and data were collected in both positive and negative ion modes. The mass spectrometry parameters were set as follows: parent ion scanning range was 70 − 1,050 m/z, with a primary mass spectrometry resolution of 1,200,000, the AGC target value of 3e6, and the maximum IT of 100 ms. The resolution of secondary mass spectrometry was 7500, the AGC target was 2e5, and the Maximum IT was 50 ms; the HCD fragmentation mode was adopted; the normalized collision energy was set to 20, 40, and 60; the isolation window was 1.5 m/z; and the scanning range of daughter ions was 200-2,000 m/z. 2.4 Metabolite identification and analysis The raw mass spectrometry data of non-targeted metabolomics were analyzed using Progenesis QI software for sample identification 19 , 20 . Features with a missing value ratio > 50.00% were removed, and backgrounds with a relative standard deviation > 30.00% in QC samples were excluded. Missing values were imputed using 1/10 of the minimum value. The quantitative information of the target metabolites was then normalized using z-scores, and the samples and metabolite expressions were clustered. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed using the ropls package ( https://bioconductor.org/packages/ropls/ ). Differentially expressed metabolites were selected based on variable projection importance (VIP > 2) and univariate t-tests ( p < 0.05). KEGG pathway annotation and enrichment analysis were conducted based on the LIPID MAPS and KEGG databases, and random forest analysis was performed using the randomForest package ( https://www.stat.berkeley.edu/~breiman/RandomForests/ ). 2.7 Integrated analysis of proteomics and metabolomics Spearman correlation analysis was used to calculate the correlation coefficients between proteins and metabolites, and significant correlations were selected using a threshold of |r| > 0.8 and p < 0.05. Pathway enrichment analysis was performed on the differentially expressed proteins and metabolites using the KEGG database, and the protein-metabolite interaction network was constructed using Cytoscape software. 2.8 Statistical analysis Statistical analysis of clinical data was performed using GraphPad Prism 9.0 (GraphPad Software Inc., Maryland, USA). For two independent sample datasets that conformed to a normal distribution, an independent samples t-test was used if variances were homogeneous; if variances were heterogeneous, a Welch-corrected t-test was performed. For non-normally distributed data, intergroup comparisons were performed using the Mann-Whitney U test. Data are presented as mean ± standard error of the mean (SEM), unless otherwise specified. p < 0.05 indicated a statistically significant difference. Results 3.1 Clinical information and treatment response A total of 10 patients undergoing fresh-cycle IVF treatment were recruited for this study. The demographic characteristics of the patients are detailed in Table 1 . As a marker of ovarian primordial follicle reserve, the baseline anti-Müllerian hormone (AMH) level on M phase was 3.70 ± 1.00 ng/ml. From the perspective of hormonal changes, compared with the M phase, follicle-stimulating hormone (FSH), estradiol (E2), progesterone (P), testosterone (T), and prolactin (PRL) levels significantly increased on OPU phase, accompanied by physiological fluctuations in luteinizing hormone (LH) levels ( Fig. 1 A-F ) . The final treatment results showed that the number of retrieved oocytes was 15.3 ± 7.73, and the total number of obtained embryos was 5.3 ± 3.34 ( Table 1 ) . Overall, this cycle protocol demonstrated clinical efficacy in terms of follicle development synchrony and embryo retrieval efficiency. Table 1 The clinical information of the IVF fresh cycle treatment. Characteristics Value (mean ± standard deviation) Age 34.8 ± 3.94 BMI (kg/m 2 ) 21.77 ± 1.75 Infertility years (year) 4.1 ± 4.58 Anti-Müllerian hormone (AMH, ng/ml) 3.70 ± 1.00 No. of oocyte retrieved 15.3 ± 7.73 No. of embryos 5.3 ± 3.34 3.2 Dynamic changes of serum proteins in patients with the GnRH antagonist protocol To investigate the dynamic changes in the proteome during ovulation induction under the GnRH antagonist protocol, quantitative proteomics analysis was performed on serum from the same patients during the M and OPU phases. A total of 5,722 proteins were identified, with over 80% matching only 1–2 peptides ( Fig. 2 A ) . Venn diagram analysis showed that 3,866 proteins were shared between the samples at the two time points, with 956 and 900 unique proteins detected in the M and OPU groups, respectively, suggesting that the ovulation induction process triggered stage-specific remodeling of the blood proteome ( Fig. 2 B ) . Using the screening criteria of |log₂(OPU/M)| ≥ 1 and P < 0.05, a total of 83 differentially expressed proteins were identified, including 37 upregulated proteins and 46 downregulated proteins ( Fig. 2 C ) . The heatmap revealed distinct group differences in the protein expression profiles between the two groups ( Fig. 2 D ) . Further KEGG functional enrichment analysis indicated that they were significantly enriched in signaling pathways such as cell cycle, AMPK, oocyte meiosis, and PI3K-Akt pathways ( Fig. 2 E ) . Protein-protein interaction (PPI) network analysis demonstrated the interactions among the differentially expressed proteins. RPL23, RPS15, and TCP1 each had significant interactions with 9 proteins, while CCT2, PRDX1, CFL1, and GSPT1 interacted with 8, 7, 6, and 6 proteins, respectively ( Fig. 2 F ) . GTPBP4, YWHAG, and UQCRC2 all had significant interactions with 5 proteins. According to the KEGG database, these proteins covered translation initiation, telomere length regulation, peroxisome, and oocyte meiosis ( Table 2 ) . These core proteins might mediate signal transduction through complex networks, synergistically supporting the follicle development process from the M phase to the OPU phase. Table 2 Function of core differentially expressed proteins during the M and OPU phases of IVF fresh cycle treatment NCBI ID Protein Description Regulation Role 9349 RPL23 Ribosomal protein L23 Up Translation initiation 6209 RPS15 Ribosomal protein S15 Down Translation initiation 6950 TCP1 T-complex 1 Up Telomere length regulation 10576 CCT2 Chaperonin containing TCP1 subunit 2 Down Telomere length regulation 5052 PRDX1 Peroxiredoxin 1 Up Peroxisome 1072 CFL1 Cofilin 1 Down Axon guidance 2935 GSPT1 G1 to S phase transition 1 Down mRNA surveillance pathway 23560 GTPBP4 GTP binding protein 4 Up Ribosome biogenesis in eukaryotes 7532 YWHAG Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma Down Cell cycle; Oocyte meiosis; PI3K-Akt signaling pathway 7385 UQCRC2 Ubiquinol-cytochrome c reductase core protein 2 Up Oxidative phosphorylation 3.3 Dynamic regulatory characteristics of serum metabolomics To explore the dynamic regulatory characteristics of the serum metabolome during ovulation induction under the GnRH antagonist protocol, non-targeted metabolomics analysis was performed. OPLS-DA analysis showed that samples from M and OPU phases clustered into distinct groups under both POS and NEG modes, indicating that ovulation induction drove stage-specific restructuring of serum metabolome (Supplementary Fig. 1) . Metabolite identification revealed 2,444 and 1,070 metabolites in the POS and NEG modes, respectively. Among them, lipids and lipid-like molecules accounted for the highest proportion (~ 32% in the POS mode and 19% in the NEG mode), followed by organic acids, organoheterocyclic compounds, benzenoids, organic oxygen, and phenylpropanoids and polyketides ( Fig. 3 A ) . All metabolites were shared between the two groups, with no stage-specific metabolites identified, suggesting that the metabolic changes induced by ovulation induction were primarily driven by abundance regulation rather than species replacement. Further differential metabolite analysis indicated that metabolic changes were more pronounced in the positive ion mode. A total of 45 significantly differential metabolites were obtained in POS mode, including 28 that were highly expressed in the M phase and 17 highly expressed in the OPU phase ( Fig. 3 B ) . The metabolites with the greatest expression changes were 3.33_528.3159 m/z (HMDB0249909; Galactocerebroside) and 4.56_257.2257 m/z (HMDB0005935; Androstenol). In the NEG mode, 21 significantly differentially metabolites were identified, with 8 relatively highly expressed in the M phase and 13 relatively highly expressed in the OPU phase, with the largest expression changes of 5.27_511.2921 m/z (HMDB0247360; Pregnanetriol 3a-O-β-D-glucuronide) and 8.99_250.0934m/z (HMDB0036394; N-Methylcalystegine C1) ( Fig. 3 C ) . KEGG functional enrichment analysis indicated that these differentially metabolites were significantly enriched in core pathways, such as the steroid hormone biosynthesis, glutathione metabolism, purine metabolism, and glycerophospholipid metabolism. A random forest model was then constructed based on all metabolites ( Fig. 3 D ) . Metabolites such as 4.56_257.2257m/z (HMDB0005935; Androstenol) in POS mode and 15.17_191.0200m/z (HMDB0000208; Oxoglutaric acid) in NEG mode showed the highest contribution, and most of them exhibited a pattern of high expression in the OPU phase and low expression in the M phase ( Fig. 3 E-F ) . These core metabolites and pathways synergistically supported the metabolic demands of follicular development and oocyte maturation during ovulation induction. 3.4 Integrated analysis reveals regulatory relationships between core proteins and metabolites Further integration of differentially expressed proteins and metabolites was performed to elucidate the molecular regulatory mechanisms underlying the ovulation induction process. A total of 576 pairwise protein-metabolite associations were identified via Spearman correlation analysis, with 192 pairs showing significant correlations ( Fig. 4 A, Supplementary Table 1) . Core association modules centered on ACACB, NUDT16, and BCL2L13 were identified, each significantly correlated with 10, 8, and 8 metabolites, respectively ( Fig. 4 B ) . MRPS6, LAMP1, YWHAQ, PPP2CB, ACACA, and PSMC3 were all significantly correlated with 7 metabolites. From the perspective of metabolites, NEG_9.83_115.0514m/z (HMDB0004101; Beta-Aminopropionitrile), POS_13.33_307.0828n (HMDB0000125; Glutathione), NEG_9.78_266.0881m/z (HMDB0000212; N-Acetylgalactosamine), and POS_14.90_311.1453m/z (HMDB0060493; N-Acetylmuramate) were all significantly associated with over 20 proteins ( Fig. 4 C ) . Combined with KEGG pathway enrichment analysis, glutathione metabolism, purine metabolism, and nucleotide sugar biosynthesis were identified as core synergistic pathways ( Fig. 4 D ) . Specifically, in the glutathione metabolism pathway, GSR (glutathione-disulfide reductase) was strongly associated with POS_13.33_307.0828n (HMDB0000125; Glutathione), and both were downregulated in the OPU phase. In the nucleotide sugar biosynthesis pathway, GFPT1 (glutamine-fructose-6-phosphate transaminase 1) was linked to the POS_14.90_311.1453m/z (HMDB0060493; N-Acetylmuramate), with consistent expression trends. In the purine metabolism pathway, NUDT16 (nudix hydrolase 16) was strongly associated with the NEG_5.42_267.0738m/z (HMDB0000195; inosine) and exhibited a negative correlation in the OPU phase. In summary, key proteins such as NUDT16 and key metabolites such as glutathione and N-acetylmuramic acid regulated the ovulation process through core pathways including glutathione metabolism and purine metabolism. Discussion Since the first successful IVF pregnancy was achieved by Professor Robert G. Edwards in the United Kingdom in 1978, the technique has undergone a critical development process, progressing from reliance on the natural cycle and the use of follicle-stimulating hormone alone to the introduction of gonadotropins 21 . With the iteration of technology, optimizing ovarian stimulation protocols has become central to enhancing ART efficacy. Clinicians must maintain follicle development efficiency while regulating FSH and LH concentrations above threshold levels to achieve synchronized development of multiple follicles, and use GnRH agonists or antagonists to prevent premature elevation of endogenous LH, which can lead to premature ovulation 22 , 23 . However, drug-induced hormone levels far exceed those in the natural cycle, increasing the risk of OHSS. Against this background, the GnRH antagonist protocol has emerged as a unique advantage. Adding GnRH antagonists in the middle and late stages of ovulation induction can rapidly inhibit the premature occurrence of endogenous LH surges, and avoid premature luteinization of follicles 6 , 10 , 24 . Additionally, it offers the benefits of a shorter treatment cycle and a lower incidence of OHSS, making it particularly suitable for patients with good ovarian reserve 25 . Furthermore, the European society of human reproduction and embryology (ESHRE) guidelines suggest that the antagonist protocol is recommended in patients with high, normal and poor responses 24 . The success of IVF depends on the coordination of folliculogenesis, oocyte maturation, and endocrine regulation 26 . However, the dynamic mechanism of blood molecules during ovulation induction has not been fully elucidated. This study analyzed the clinical response characteristics and molecular regulatory network of ovulation induction from the M cycle to the OPU day by integrating clinical index, serum proteomics, and metabolomics analysis, providing new insights into the molecular mechanisms supporting follicular development and treatment efficacy under the GnRH antagonist protocol. Ovarian reserve is a core indicator for assessing female fertility potential, and is crucial for selecting ART protocols and predicting treatment efficacy. AMH, a hormone specifically secreted by granulosa cells of growing follicles, directly reflects the quantity and quality of follicles in the ovary and is a globally recognized key biomarker for ovarian reserve 27 . The baseline AMH levels of patients enrolled in this study fell within the range of good ovarian reserve as defined by clinical consensus 28 . From the perspective of endocrine dynamics, hormone levels such as FSH, E2, and P were significantly elevated during the OPU phase compared to the M phase, consistent with the physiological patterns of follicular maturation during ovulation induction. Elevated FSH levels were the core driver of follicle recruitment and dominant follicle selection 29 . The addition of antagonists inhibits the endogenous LH peaks, but does not affect follicle hormone synthesis function 30 . E2 is synthesized by granulosa cells, and its accumulation reflects the functional activity of follicular granulosa cells, which was directly correlated with the clinical outcome of 15.3 oocytes retrieved. Previous studies have confirmed that there was a positive correlation between E2 level and the number of oocytes retrieved, with a more significant correlation in patients with good ovarian reserve 31 . Higher levels of P and T are associated with the initiation of corpus luteum formation and the activation of theca cell function, further supporting the synchrony and maturity of follicular development 32 , 33 . In addition, the physiological fluctuation of LH further supports the normal regulatory feedback of the hypothalamic-pituitary-ovarian axis, indicating that the GnRH antagonist protocol effectively triggers the synergistic activation of the endocrine network 34 . The final treatment outcomes showed an average of 15.3 retrieved oocytes and 5.3 retrieved embryos, fully demonstrating that the antagonist protocol used in this study has reliable clinical efficacy. These results were basically consistent with reports from similar studies at home and abroad, with the number of oocytes retrieved slightly exceeding the average level, further highlighting the effectiveness of the protocol 35 , 36 . During the complex physiological process of ovulation induction, dynamic changes in serum proteins serve as molecular markers responding to treatment. Throughout ovulation induction, the serum proteome exhibited significant stage-specific alterations, with more than 900 unique proteins identified in M and OPU phases, respectively. This finding suggested that the body maintained its basic physiological functions, and responded to ovulation induction by selectively remodeling. Differentially expressed proteins were significantly enriched in the cell cycle, AMPK, oocyte meiosis, and PI3K-Akt pathways, which were core regulatory pathways for follicle development and oocyte maturation. The antagonist protocol ensured follicular and oocyte development through the coordinated activation of these pathways. First, activation of the cell cycle pathway ensured the proliferation and division of granulosa cells and oocytes, providing the cellular foundation for follicle growth 37 . Second, the AMPK pathway, functioning as an energy metabolism switch, regulated glucose and lipid metabolism to provide energy for follicle development, thus adapting to the increased energy demands of follicles under the antagonist protocol 38 , 39 . Finally, the PI3K-Akt pathway participated in the initiation and resumption of oocyte meiosis, regulating oocyte maturation indirectly 40 . Core proteins identified via PPI analysis further revealed critical molecular nodes. Ribosomal proteins RPL23 and RPS15 participate in translation initiation, and their differential expression may be associated with the increased demand for protein synthesis during granulosa cell proliferation 41 . Members of the T-complex family, TCP1 and CCT2, support the folding and functional performance of meiosis-related proteins through their chaperone activity, thus indirectly regulating the meiotic process 42 , 43 . Peroxidase PRDX1 may maintain the redox balance of oocytes by scavenging reactive oxygen species 41 . Additionally, YWHAG was involved in both the cell cycle and oocyte meiosis pathways, suggesting that it may act as a cross-regulatory node to coordinate signals for cell division and maturation. These critical proteins collectively form the molecular basis for follicular development through complex interaction networks. As a technology for capturing global metabolic changes, metabolomics dynamically reflects the body’s essential demands through its own variations. Non-targeted metabolomic analysis showed significant clustering differences between M-phase and OPU-phase samples in both positive and negative ion modes. Unlike the proteome, changes in the metabolome were dominated by abundance regulation, with no stage-specific metabolites identified. This suggested that the body met the metabolic requirements of ovulation induction through the dynamic allocation of existing metabolites rather than the synthesis of new substances. This regulatory pattern could maintain the body’s metabolic homeostasis while satisfying the needs of follicular development, and it also explained from a metabolic perspective why the antagonist protocol was more effective in maintaining metabolic balance in patients compared with other protocols 44 , 45 . In terms of metabolite classification, lipids and lipid-like molecules accounted for the highest proportion of metabolites, which may be related to the role of lipids as energy reserves and precursors for steroid hormone synthesis during follicular development 46 . Moreover, ATP produced by lipid metabolism served as the main energy source for follicular development 47 . Under the POS mode, galactocerebroside (HMDB0249909), which showed the most significant expression change, regulated membrane fluidity by interacting with other sphingolipids, affecting sperm motility, and endometrial receptivity 48 . Pregnanetriol 3a-O-β-D-glucuronide (HMDB0247360) under the NEG mode, the metabolite with the most prominent difference, was a progesterone metabolite, and its high expression confirmed the initiation of corpus luteum function during the OPU phase 49 . The significant enrichment of differentially expressed metabolites in the steroid hormone biosynthesis pathway further reflected the metabolic mechanisms underlying the elevated sex hormone levels during the OPU phase. Glutathione, as the primary antioxidant, maintained the redox balance of oocytes through changes in its abundance, which synergized with the upregulation of PRDX1 in the proteome 50 . Purin metabolism, as an important intracellular metabolic pathway, provided raw materials for nucleic acid synthesis during follicular cell proliferation, jointly ensuring follicular development 51 . Abnormal purine metabolism could exacerbate oxidative stress, inhibit granulosa cell proliferation, and induce follicular atresia 52 . Finally, core metabolites identified by the random forest model, such as androstenol and oxoglutaric acid, were highly expressed in the OPU phase, suggesting that they might serve as potential markers for evaluating the efficacy of ovulation induction under the antagonist protocol. Lastly, the synergistic links underlying the molecular mechanism of ovulation induction by the antagonist protocol were elucidated through integrated analysis of proteins and metabolites. Spearman correlation analysis identified 192 pairs of significantly correlated protein-metabolite associations, and three core synergistic pathways were recognized, including glutathione metabolism, purine metabolism, and nucleotide sugar biosynthesis. The module centered on NUDT16 showed a negative correlation with inosine, an intermediate product of purine metabolism, suggesting that NUDT16 may maintain oocyte energy supply by regulating purine homeostasis. Inosine could either provide energy through decomposition or be converted into adenine to participate in DNA synthesis 53 , 54 . The negative regulation of NUDT16 prevented the excessive inosine decomposition, ensuring the supply of nucleic acid raw materials required for follicular cell proliferation 50 . The strong association between GSR and glutathione, along with their co-downregulation, reflects the dynamic adjustment of antioxidant requirements during the OPU phase, activating oocyte meiosis-related signaling pathways 55 . The synergistic effects of these core pathways and molecules collectively formed a molecular network involving hormone synthesis, oxidative homeostasis, and energy supply. These networks explained how the antagonist protocol met the comprehensive demands of follicular development and oocyte maturation through the dynamic regulation of proteins and metabolites. This study still has certain limitations. First, the small sample size might restrict the generalizability of the results. Second, the functions of differential molecules were not verified, and some regulatory relationships required further confirmation. Additionally, no comparison was made with other stimulation protocols, so it was difficult to determine whether the molecular regulatory network identified in this study was unique to the GnRH antagonist protocol. Future research should expand the sample size and include multi-center data to verify the stability of core biomarkers. Meanwhile, cell models or animal experiments should be used to clarify the specific functions of key molecules, thereby providing more direct evidence for the elaboration of the mechanism. Conclusions Overall, this study systematically revealed the molecular dynamic characteristics of ovulation induction during the IVF fresh cycles through integrated serum proteomic and metabolomic analyses. This study confirmed that the GnRH antagonist protocol exhibited favorable clinical efficacy in the treatment of such IVF fresh cycles. Notably, this study found that ovulation induction under this protocol regulated the dynamic changes of the serum proteome and metabolome for the first time, constructing a molecular regulatory network centered on glutathione metabolism, and purine metabolism. These networks supported follicular development and oocyte maturation through the synergistic effect of multiple pathways. Collectively, the results not only verified the clinical effectiveness of the GnRH antagonist protocol but also offered potential molecular targets and biomarkers for the optimizing follicular maturation monitoring indicators and improving of IVF treatment outcomes in clinical practice. Declarations Ethics approval and consent to participate This study was reviewed and approved by the ethics committee of Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine. Consent for publication All research subjects have signed informed consent forms. Clinical trial number Not applicable. Competing interests The authors have no conflicts of interest to declare. Available of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Funding Nil Author contributions Conception: M. Tao; Data Curation: H. Shao, N. Wang; Formal analysis: TM. Ye; Methodology: M. Tao; Validation: H. 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Glutathione protects against the meiotic defects of ovine oocytes induced by arsenic exposure via the inhibition of mitochondrial dysfunctions. Ecotoxicol Environ Saf 230 , 113135 (2022). https://doi.org:10.1016/j.ecoenv.2021.113135 Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Supplementary Figure 1. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) analysis of metabolites in the positive (POS) and negative (NEG) ion modes. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7779262","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587406959,"identity":"33da9c40-8b37-42ef-9b76-c33490d4acdb","order_by":0,"name":"Tian-Min Ye","email":"","orcid":"","institution":"Shanghai Sixth People’s Hospital, Shanghai Jiaotong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tian-Min","middleName":"","lastName":"Ye","suffix":""},{"id":587406960,"identity":"b04ee542-d209-4aeb-9c3a-c07a15750036","order_by":1,"name":"Hongfang Shao","email":"","orcid":"","institution":"Shanghai Sixth People’s Hospital, Shanghai Jiaotong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongfang","middleName":"","lastName":"Shao","suffix":""},{"id":587406961,"identity":"608e7c7a-ad90-48fc-9e32-174aed0f2a15","order_by":2,"name":"Na Wang","email":"","orcid":"","institution":"Shanghai Sixth People’s Hospital, Shanghai Jiaotong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Wang","suffix":""},{"id":587406962,"identity":"eadf8b4b-1a01-4b13-9cd3-3a6632064239","order_by":3,"name":"Minfang Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYLCCigoGZjCDhxjVYEVnzjAw85Cm5WwblEGUFnv2w0c3HJxXx24vkcD44G0bg7w5QVt40tJuHNx2mJlHIoHZcG4bg+HOBoIOyzG7/XHbAZAWNmneNoYEgwOEtPC/MbtxcE4dSAv7b+K0SOQAtTQwg21hJk7LjWdpNw4cA/rlzMNmyTnnJAw3ENLC3p987MaBmrpk9vbkgx/elNnIE7QFBpIZGBgbgLQEkeqBwI54paNgFIyCUTDiAAA2ejxsRdWGlQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Sixth People’s Hospital, Shanghai Jiaotong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Minfang","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2025-10-04 10:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7779262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7779262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102260793,"identity":"06e0b24b-42fe-4932-a10d-cde86a4042aa","added_by":"auto","created_at":"2026-02-10 00:34:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of blood hormone levels between patient's menstrual cycle (M) and ovum pick up day (OPU).\u003c/strong\u003e A. Follicle-stimulating hormone (FSH). B. Estradiol (E2). C. Progesterone (P). D. Testosterone (T). E. Prolactin (PRL). F. Luteinizing hormone (LH). Data are presented as mean ± SEM. ** indicates \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01; *** indicates \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7779262/v1/2beb53d5fc37b191c68cfc36.png"},{"id":102297630,"identity":"d54ca21d-a478-4d9c-afd1-862c64614df8","added_by":"auto","created_at":"2026-02-10 10:28:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1477246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of plasma protein dynamics during the M and OPU phases in patients under the GnRH antagonist protocol. \u003c/strong\u003eA. Peptide count distribution of identified proteins. B. Venn diagram showing the overlapping protein profiles between the M and OPU phases. C. Volcano plot showing the distribution of differentially expressed proteins. Red dots indicate upregulated proteins, blue dots indicate downregulated proteins, and gray dots indicate stably expressed proteins. D. Heatmap showing the expression clustering patterns of differentially expressed proteins. E. KEGG enrichment analysis of differentially expressed proteins. Bubble size indicates the number of proteins enriched in the pathway and the color gradient reflects \u003cem\u003ep\u003c/em\u003evalue significance. F. Interaction network among differentially expressed proteins. Red nodes represent upregulated proteins, blue nodes represent downregulated proteins, and lines indicate protein-protein interactions.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7779262/v1/b645083c25b1d8627d332e16.png"},{"id":102260796,"identity":"11c763f9-5562-4a2f-be28-8fdbdf293076","added_by":"auto","created_at":"2026-02-10 00:34:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2021335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential analysis of plasma metabolomics between the M and OPU phases in patients with the GnRH antagonist protocol. \u003c/strong\u003eA. The ring diagram shows the distribution of metabolite categories. The outer ring represents the positive ion mode (POS), and the inner ring represents the negative ion mode (NEG). B-C. Volcano plots showing differential metabolites in POS (B) and NEG (C) modes. Red dots represent metabolites upregulated during the OPU phase, orange dots represent downregulated metabolites, and blue dots indicate no difference.\u003cstrong\u003e \u003c/strong\u003eD. KEGG functional enrichment analysis of differential metabolites. Bubble size represents the number of metabolites, with redder colors indicating higher significance. E-F. Random forest analysis of all detected metabolites in POS (E) and NEG (F) modes. The x-axis represents the contribution of metabolites to group classification, with higher values indicating stronger indicative power. The color blocks on the right indicate the content of the top 15 metabolites in different groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7779262/v1/f3e59117d669a5a71c5b4504.png"},{"id":102260797,"identity":"9bcce7da-5582-4628-a757-fe8ba7dc979b","added_by":"auto","created_at":"2026-02-10 00:34:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2618283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation analysis of the serum proteome and metabolome. \u003c/strong\u003eA. Spearman correlation heatmap. Rows represent metabolites, and columns represent proteins. B. Protein-mediated core metabolites association network. Core proteins associated with ≥ 7 metabolites were selected. Red triangles represent proteins, and blue circles represent metabolites; edges between nodes indicate significant correlations (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). C. Metabolite-mediated core protein association network. Core metabolites associated with ≥ 20 proteins were selected. Blue circles represent metabolites, and red triangles represent proteins. D. Sankey diagram of protein-pathway-metabolite associations. The left side shows differential proteins, the middle shows enriched pathways, and the right side shows differential metabolites; the width of the connecting lines corresponds to the strength of the associations.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7779262/v1/8749723bf934f167f0f0b4fc.png"},{"id":103717186,"identity":"6adf9aa0-28bb-4a58-8eb1-08939465fe32","added_by":"auto","created_at":"2026-03-02 06:11:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6757696,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7779262/v1/7e109243-cfbe-47e7-8582-ecf437861f1f.pdf"},{"id":102260794,"identity":"391ce660-8237-41a5-b144-6ec9ce4f9ea0","added_by":"auto","created_at":"2026-02-10 00:34:15","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":485252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) analysis of metabolites in the positive (POS) and negative (NEG) ion modes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7779262/v1/402d3f65555815a5974c62be.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum Proteomic and Metabolomic Profiling Uncovers Molecular Mechanisms in Patients Undergoing In-Vitro Fertilization with the GnRH Antagonist Protocol","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfertility is a global health issue which affects more than millions of couples worldwide. According to global estimations, the condition of infertility affected a substantial number of individuals, reaching an approximate 48.5\u0026nbsp;million people globally \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In China, the prevalence of infertility was 25% among couples of reproductive ages \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The application of assisted reproductive technology (ART) has become increasingly common for treating of infertility. With the development of ART, it is necessary for the doctors to choose an ovarian stimulation protocol that is safe and efficient for the patients. Nowadays, the gonadotropin-releasing hormone (GnRH) antagonist protocol has become widely used in clinical ART practice \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The advantages of the GnRH antagonist protocol include short duration of administration, low cost, flexible regimen, rapid pituitary recovery, and no \"flare-up\" effect \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The GnRH antagonist protocol not only effectively suppresses spontaneous early luteinizing hormone (LH) surge and increases the number of oocytes retrieved but also significantly reduces the occurrence of ovarian hyperstimulation syndrome (OHSS) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In recent years, some studies have reported that there was no significant difference between live birth and ongoing pregnancy rates between optimized GnRH agonist and antagonist protocols, which has contributed to its growing clinical application \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough the clinical value of the GnRH antagonist protocol has been recognized, there remains a significant gap in the understanding of the dynamic molecular changes occurring in patients during its therapy. Usually, higher serum steroid hormone levels than those of a those of a natural menstrual cycle are detected during controlled ovarian stimulation \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The protein expression in serum fluctuates after the treatment, and it should reveal the mechanism underlying physiological responses to ovarian stimulation, such as OHSS \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, current research on the specific changes in proteins and metabolites in patients and their potential impacts during GnRH antagonist treatment remains limited. Most existing literature focuses on a single biomolecular level, lacking integrated proteomic and metabolomic analysis, making it difficult to fully reveal the molecular network regulatory mechanism underlying drug intervention \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, most studies only adopt static detection methods and fail to dynamically capture the temporal variation characteristics of metabolic pathways \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Although some studies have focused on changes in physiological indicators during in-vitro fertilization (IVF) treatment, systematic research on the proteomic and metabolomic characteristics at two key time points, the menstrual cycle and ovum pick up day, in patients receiving GnRH antagonist treatment remains relatively insufficient \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These two time points not only correspond to the period of drastic hormonal fluctuations in the female reproductive cycle, but also serve as key nodes for the GnRH antagonist to exert its clinical effects. The lack of understanding of their molecular characteristics makes it impossible to clarify the specific impact of the drug on physiological processes such as neuroendocrine regulation and energy metabolism remodeling, which severely limits the in-depth understanding of the therapeutic mechanism of this protocol and the optimization of individualized protocols \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to collect serum samples from IVF patients undergoing GnRH antagonist treatment at M and OPU phases, and conducted integrated proteomic and metabolomic analyses. By exploring the dynamic variation patterns of proteins and metabolites in patients, this study sought to provide important theoretical support and experimental evidence for elucidating the molecular mechanism by which GnRH antagonists regulated the hypothalamic-pituitary-ovarian axis, predicting patients\u0026rsquo; ovarian responsiveness, and improving the clinical pregnancy rate of IVF.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient inclusion criteria\u003c/h2\u003e \u003cp\u003eThis study recruited 10 women who received the GnRH antagonist protocol in the Reproductive Medicine Center of Shanghai Sixth People\u0026rsquo;s Hospital Affiliated to Shanghai Jiaotong University School of Medicine. Inclusion criteria included undergoing GnRH antagonist IVF/ICSI (Intra-cytoplasmic sperm injection) protocol; first fresh stimulation cycle; male factor infertility in the couple; complete clinical and follow-up data were available during the treatment cycle; and no prior history of IVF treatment. Exclusion criteria included the presence of malignant tumors, abnormal immune function, recent use of medications affecting protein and metabolism, history of mental illness, and any other conditions not meeting the above inclusion criteria. This study has been approved by the ethics committee of Shanghai Sixth People\u0026rsquo;s Hospital Affiliated to Shanghai Jiaotong University School of Medicine, and all research subjects have signed informed consent forms. All procedures involving human participants were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 GnRH antagonist protocol for ovulation induction\u003c/h2\u003e \u003cp\u003eAll participants underwent ovulation induction therapy using a GnRH antagonist protocol. Gonadotropin (Gn) injections were administered subcutaneously starting on day 2 of the menstrual cycle, with concurrent monitoring of serum levels of follicle stimulating hormone (FSH), estradiol (E), progesterone (P), and LH. A transvaginal ultrasound was performed to assess antral follicle count (AFC). The initial dose of recombinant FSH (r-FSH) was adjusted based on the AFC results. If AFC\u0026thinsp;\u0026le;\u0026thinsp;6, 300 IU was administered to initiate superovulation; if 6\u0026thinsp;\u0026lt;\u0026thinsp;AFC\u0026thinsp;\u0026lt;\u0026thinsp;15, 225 IU was administered; if AFC\u0026thinsp;\u0026ge;\u0026thinsp;15, 150 IU was administered. The r-FSH was chosen from Gonal-f (Merck Serono, Germany) or Puregon (Merck Sharp \u0026amp; Dohme, USA). As a fixed antagonist protocol, subcutaneous injections of Ganirelix acetate (0.25 mg/day, Merck Sharp \u0026amp; Dohme, USA) were initiated on day 6 of r-FSH administration. When the average diameter of one dominant follicle was \u0026ge;\u0026thinsp;18 mm, and the average diameter of the other two dominant follicles was \u0026ge;\u0026thinsp;16 mm, the administration of gonadotropin (Gn) was stopped, and human chorionic gonadotropin (hCG) was administered, followed by oocyte retrieval 36 hours later. Blood samples were collected both on day 2 of the menstrual cycle (M) and on the day of ovum pick up (OPU). After blood collection, serum samples were separated for subsequent testing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Protein extraction and liquid chromatography-mass spectrometry (LC-MS) analysis\u003c/h2\u003e \u003cp\u003eAn appropriate amount of serum samples was mixed with 50 mM ammonium bicarbonate buffer, and the protein was inactivated at 95\u0026deg;C for 3 min. After cooling, trypsin was added at an enzyme-to-protein mass ratio of 1: 25, and digestion was performed at 37\u0026deg;C for 16 h. Subsequently, the peptides were extracted and dried. Finally, the peptides were reconstituted with 100 \u0026micro;L of 0.1% formic acid solution and separated and identified on an SDS-PAGE gel.\u003c/p\u003e \u003cp\u003eThe samples were separated using a high-performance liquid chromatography system (EASY-nLC 1200, Thermo Fisher Scientific, USA) with a nanoliter flow rate. Mobile phase A was a 0.1% formic acid aqueous solution, and mobile phase B was a 0.1% formic acid acetonitrile aqueous solution containing 80% acetonitrile. First, the injection column and analytical column were equilibrated with 100% mobile phase A. Subsequently, the enzyme-digested peptide segments of the samples were separated using C18 injection columns (injection column: 2 cm in length, 100 \u0026micro;m inner diameter, 3 \u0026micro;m particle size; analytical column: 15 cm in length, 150 \u0026micro;m inner diameter, 1.9 \u0026micro;m particle size) at a flow rate of 600 nL/min. Mass spectrometry analysis was performed using a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA) with an electrospray ionization (ESI) source in positive ion mode. The parent ion scanning range was 300\u0026ndash;1400 m/z, with a primary mass spectrometry resolution of 60,000 at 200 m/z, an automatic gain control (AGC) target value of 3e6, and a maximum injection time (IT) of 20 ms. For the secondary mass spectrometry, data-independent acquisition (DIA) mode was adopted, with 20 DIA scans collected after each full scan. The higher-energy collisional dissociation (HCD) fragmentation mode was used with a normalized collision energy of 27%. The isolation window varied according to the isolation window settings, and the secondary mass spectrometry resolution was 15,000 at 200 m/z.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Protein identification and analysis\u003c/h2\u003e \u003cp\u003eRaw data were analyzed using the iProteome data analysis cloud platform for database-based qualitative and quantitative analysis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The database search parameters were set as follows: Enzyme: Trypsin; Database: Custom library; Fixed modifications: Carbamidomethyl (C); Variable modifications: Oxidation (M) and Acetyl (Protein N-term); Missed Cleavage: 2; Peptide Mass Tolerance: 20 ppm; and Fragment Mass Tolerance:0.05 Da. The peptide false discovery rate\u0026thinsp;\u0026le;\u0026thinsp;0.05 was used as the screening criterion. After completing the database search, the peptide peak areas were normalized using z-scores and summarized as protein quantification values. The quantification data were processed to remove contaminant proteins, and impute missing values. The criteria for identifying differentially expressed proteins were a fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5 and a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after Benjamini-Hochberg correction. Protein domain features were identified from the Pfam database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pfam.xfam.org/\u003c/span\u003e\u003cspan address=\"https://pfam.xfam.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using InterProScan \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Homologous sequences of differentially expressed proteins were searched via NCBI BLAST and InterProScan, followed by mapping gene ontology (GO) terms to complete the annotation using Blast2GO \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Kyoto encyclopedia of genes and genomes (KEGG) annotation was obtained by comparing with the KEGG database and mapped to corresponding pathways. Differentially expressed proteins were then subjected to GO functional (including molecular function, cellular component, and biological process) and KEGG pathway enrichment analysis. Protein interaction networks were constructed based on the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and visualized and analyzed using Cytoscape \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 LC/MS non-targeted metabolomics analysis\u003c/h2\u003e \u003cp\u003eAn appropriate amount of sample was taken, and a pre-cooled methanol-acetonitrile solution (1:1) was added. After vortex mixing for 30 s, the mixtures were incubated at -20\u0026deg;C for 1 h. They were then centrifuged at 14,000 g for 20 min at 4\u0026deg;C, and the supernatant was transferred to a new tube. Meanwhile, a mixture of samples was taken to prepare quality control (QC) samples. Chromatographic separation was performed using a Nexera UHPLC LC-30A system (Shimadzu, Japan) equipped with a HILIC column (Waters, ACQUITY UPLC BEH Amide, 1.7 \u0026micro;m particle size, 2.1\u0026times;100 mm). The column was equilibrated with 98% mobile phase A, and the sample was loaded with a flow rate maintained at 0.3 mL/min. The elution program was as follows: initial 2% mobile phase B maintained for 0.5 min, followed by 11.5 min increase to 98% mobile phase B, held for 4 min, then returned to 2% mobile phase B within 0.1 min, and washed for 1.9 min. Among them, in the positive ion mode (POS), mobile phase A was 10 mM ammonium acetate-acetonitrile water (95:5, containing 0.1% formic acid); mobile phase B was 10 mM ammonium acetate-acetonitrile water (50:50, containing 0.1% formic acid). In the negative ion mode (NEG), mobile phase A and B were 10 mM ammonium acetate-acetonitrile water (95:5) and 10 mM ammonium acetate-acetonitrile water (50:50), respectively, both adjusted to pH 8.0 with ammonia water. The samples after chromatographic separation were analyzed by a Q Exactive HF-X mass spectrometer (Thermo Fisher, USA), and data were collected in both positive and negative ion modes. The mass spectrometry parameters were set as follows: parent ion scanning range was 70\u0026thinsp;\u0026minus;\u0026thinsp;1,050 m/z, with a primary mass spectrometry resolution of 1,200,000, the AGC target value of 3e6, and the maximum IT of 100 ms. The resolution of secondary mass spectrometry was 7500, the AGC target was 2e5, and the Maximum IT was 50 ms; the HCD fragmentation mode was adopted; the normalized collision energy was set to 20, 40, and 60; the isolation window was 1.5 m/z; and the scanning range of daughter ions was 200-2,000 m/z.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Metabolite identification and analysis\u003c/h2\u003e \u003cp\u003eThe raw mass spectrometry data of non-targeted metabolomics were analyzed using Progenesis QI software for sample identification \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Features with a missing value ratio\u0026thinsp;\u0026gt;\u0026thinsp;50.00% were removed, and backgrounds with a relative standard deviation\u0026thinsp;\u0026gt;\u0026thinsp;30.00% in QC samples were excluded. Missing values were imputed using 1/10 of the minimum value. The quantitative information of the target metabolites was then normalized using z-scores, and the samples and metabolite expressions were clustered. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed using the ropls package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/ropls/\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/ropls/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differentially expressed metabolites were selected based on variable projection importance (VIP\u0026thinsp;\u0026gt;\u0026thinsp;2) and univariate t-tests (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). KEGG pathway annotation and enrichment analysis were conducted based on the LIPID MAPS and KEGG databases, and random forest analysis was performed using the randomForest package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stat.berkeley.edu/~breiman/RandomForests/\u003c/span\u003e\u003cspan address=\"https://www.stat.berkeley.edu/~breiman/RandomForests/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Integrated analysis of proteomics and metabolomics\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was used to calculate the correlation coefficients between proteins and metabolites, and significant correlations were selected using a threshold of |r| \u0026gt; 0.8 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Pathway enrichment analysis was performed on the differentially expressed proteins and metabolites using the KEGG database, and the protein-metabolite interaction network was constructed using Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis of clinical data was performed using GraphPad Prism 9.0 (GraphPad Software Inc., Maryland, USA). For two independent sample datasets that conformed to a normal distribution, an independent samples t-test was used if variances were homogeneous; if variances were heterogeneous, a Welch-corrected t-test was performed. For non-normally distributed data, intergroup comparisons were performed using the Mann-Whitney U test. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM), unless otherwise specified. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinical information and treatment response\u003c/h2\u003e \u003cp\u003eA total of 10 patients undergoing fresh-cycle IVF treatment were recruited for this study. The demographic characteristics of the patients are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As a marker of ovarian primordial follicle reserve, the baseline anti-M\u0026uuml;llerian hormone (AMH) level on M phase was 3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00 ng/ml. From the perspective of hormonal changes, compared with the M phase, follicle-stimulating hormone (FSH), estradiol (E2), progesterone (P), testosterone (T), and prolactin (PRL) levels significantly increased on OPU phase, accompanied by physiological fluctuations in luteinizing hormone (LH) levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-F\u003cb\u003e)\u003c/b\u003e. The final treatment results showed that the number of retrieved oocytes was 15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73, and the total number of obtained embryos was 5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Overall, this cycle protocol demonstrated clinical efficacy in terms of follicle development synchrony and embryo retrieval efficiency.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical information of the IVF fresh cycle treatment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility years (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-M\u0026uuml;llerian hormone (AMH, ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of oocyte retrieved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Dynamic changes of serum proteins in patients with the GnRH antagonist protocol\u003c/h2\u003e \u003cp\u003eTo investigate the dynamic changes in the proteome during ovulation induction under the GnRH antagonist protocol, quantitative proteomics analysis was performed on serum from the same patients during the M and OPU phases. A total of 5,722 proteins were identified, with over 80% matching only 1\u0026ndash;2 peptides \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Venn diagram analysis showed that 3,866 proteins were shared between the samples at the two time points, with 956 and 900 unique proteins detected in the M and OPU groups, respectively, suggesting that the ovulation induction process triggered stage-specific remodeling of the blood proteome \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Using the screening criteria of |log₂(OPU/M)| \u0026ge; 1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, a total of 83 differentially expressed proteins were identified, including 37 upregulated proteins and 46 downregulated proteins \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The heatmap revealed distinct group differences in the protein expression profiles between the two groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Further KEGG functional enrichment analysis indicated that they were significantly enriched in signaling pathways such as cell cycle, AMPK, oocyte meiosis, and PI3K-Akt pathways \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eProtein-protein interaction (PPI) network analysis demonstrated the interactions among the differentially expressed proteins. RPL23, RPS15, and TCP1 each had significant interactions with 9 proteins, while CCT2, PRDX1, CFL1, and GSPT1 interacted with 8, 7, 6, and 6 proteins, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. GTPBP4, YWHAG, and UQCRC2 all had significant interactions with 5 proteins. According to the KEGG database, these proteins covered translation initiation, telomere length regulation, peroxisome, and oocyte meiosis \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These core proteins might mediate signal transduction through complex networks, synergistically supporting the follicle development process from the M phase to the OPU phase.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunction of core differentially expressed proteins during the M and OPU phases of IVF fresh cycle treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCBI ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRole\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRPL23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRibosomal protein L23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTranslation initiation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRPS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRibosomal protein S15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTranslation initiation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-complex 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTelomere length regulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChaperonin containing TCP1 subunit 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTelomere length regulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRDX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeroxiredoxin 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeroxisome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCofilin 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAxon guidance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSPT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG1 to S phase transition 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emRNA surveillance pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTPBP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTP binding protein 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRibosome biogenesis in eukaryotes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYWHAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein gamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCell cycle; Oocyte meiosis; PI3K-Akt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUQCRC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUbiquinol-cytochrome c reductase core protein 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOxidative phosphorylation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Dynamic regulatory characteristics of serum metabolomics\u003c/h2\u003e \u003cp\u003eTo explore the dynamic regulatory characteristics of the serum metabolome during ovulation induction under the GnRH antagonist protocol, non-targeted metabolomics analysis was performed. OPLS-DA analysis showed that samples from M and OPU phases clustered into distinct groups under both POS and NEG modes, indicating that ovulation induction drove stage-specific restructuring of serum metabolome \u003cb\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/b\u003e. Metabolite identification revealed 2,444 and 1,070 metabolites in the POS and NEG modes, respectively. Among them, lipids and lipid-like molecules accounted for the highest proportion (~\u0026thinsp;32% in the POS mode and 19% in the NEG mode), followed by organic acids, organoheterocyclic compounds, benzenoids, organic oxygen, and phenylpropanoids and polyketides \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. All metabolites were shared between the two groups, with no stage-specific metabolites identified, suggesting that the metabolic changes induced by ovulation induction were primarily driven by abundance regulation rather than species replacement. Further differential metabolite analysis indicated that metabolic changes were more pronounced in the positive ion mode. A total of 45 significantly differential metabolites were obtained in POS mode, including 28 that were highly expressed in the M phase and 17 highly expressed in the OPU phase \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The metabolites with the greatest expression changes were 3.33_528.3159 m/z (HMDB0249909; Galactocerebroside) and 4.56_257.2257 m/z (HMDB0005935; Androstenol). In the NEG mode, 21 significantly differentially metabolites were identified, with 8 relatively highly expressed in the M phase and 13 relatively highly expressed in the OPU phase, with the largest expression changes of 5.27_511.2921 m/z (HMDB0247360; Pregnanetriol 3a-O-β-D-glucuronide) and 8.99_250.0934m/z (HMDB0036394; N-Methylcalystegine C1) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. KEGG functional enrichment analysis indicated that these differentially metabolites were significantly enriched in core pathways, such as the steroid hormone biosynthesis, glutathione metabolism, purine metabolism, and glycerophospholipid metabolism. A random forest model was then constructed based on all metabolites \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Metabolites such as 4.56_257.2257m/z (HMDB0005935; Androstenol) in POS mode and 15.17_191.0200m/z (HMDB0000208; Oxoglutaric acid) in NEG mode showed the highest contribution, and most of them exhibited a pattern of high expression in the OPU phase and low expression in the M phase \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F\u003cb\u003e)\u003c/b\u003e. These core metabolites and pathways synergistically supported the metabolic demands of follicular development and oocyte maturation during ovulation induction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Integrated analysis reveals regulatory relationships between core proteins and metabolites\u003c/h2\u003e \u003cp\u003eFurther integration of differentially expressed proteins and metabolites was performed to elucidate the molecular regulatory mechanisms underlying the ovulation induction process. A total of 576 pairwise protein-metabolite associations were identified via Spearman correlation analysis, with 192 pairs showing significant correlations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cb\u003eSupplementary Table\u0026nbsp;1)\u003c/b\u003e. Core association modules centered on ACACB, NUDT16, and BCL2L13 were identified, each significantly correlated with 10, 8, and 8 metabolites, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. MRPS6, LAMP1, YWHAQ, PPP2CB, ACACA, and PSMC3 were all significantly correlated with 7 metabolites. From the perspective of metabolites, NEG_9.83_115.0514m/z (HMDB0004101; Beta-Aminopropionitrile), POS_13.33_307.0828n (HMDB0000125; Glutathione), NEG_9.78_266.0881m/z (HMDB0000212; N-Acetylgalactosamine), and POS_14.90_311.1453m/z (HMDB0060493; N-Acetylmuramate) were all significantly associated with over 20 proteins \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Combined with KEGG pathway enrichment analysis, glutathione metabolism, purine metabolism, and nucleotide sugar biosynthesis were identified as core synergistic pathways \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Specifically, in the glutathione metabolism pathway, GSR (glutathione-disulfide reductase) was strongly associated with POS_13.33_307.0828n (HMDB0000125; Glutathione), and both were downregulated in the OPU phase. In the nucleotide sugar biosynthesis pathway, GFPT1 (glutamine-fructose-6-phosphate transaminase 1) was linked to the POS_14.90_311.1453m/z (HMDB0060493; N-Acetylmuramate), with consistent expression trends. In the purine metabolism pathway, NUDT16 (nudix hydrolase 16) was strongly associated with the NEG_5.42_267.0738m/z (HMDB0000195; inosine) and exhibited a negative correlation in the OPU phase. In summary, key proteins such as NUDT16 and key metabolites such as glutathione and N-acetylmuramic acid regulated the ovulation process through core pathways including glutathione metabolism and purine metabolism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince the first successful IVF pregnancy was achieved by Professor Robert G. Edwards in the United Kingdom in 1978, the technique has undergone a critical development process, progressing from reliance on the natural cycle and the use of follicle-stimulating hormone alone to the introduction of gonadotropins \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. With the iteration of technology, optimizing ovarian stimulation protocols has become central to enhancing ART efficacy. Clinicians must maintain follicle development efficiency while regulating FSH and LH concentrations above threshold levels to achieve synchronized development of multiple follicles, and use GnRH agonists or antagonists to prevent premature elevation of endogenous LH, which can lead to premature ovulation \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, drug-induced hormone levels far exceed those in the natural cycle, increasing the risk of OHSS. Against this background, the GnRH antagonist protocol has emerged as a unique advantage. Adding GnRH antagonists in the middle and late stages of ovulation induction can rapidly inhibit the premature occurrence of endogenous LH surges, and avoid premature luteinization of follicles \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Additionally, it offers the benefits of a shorter treatment cycle and a lower incidence of OHSS, making it particularly suitable for patients with good ovarian reserve \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Furthermore, the European society of human reproduction and embryology (ESHRE) guidelines suggest that the antagonist protocol is recommended in patients with high, normal and poor responses \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The success of IVF depends on the coordination of folliculogenesis, oocyte maturation, and endocrine regulation \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, the dynamic mechanism of blood molecules during ovulation induction has not been fully elucidated. This study analyzed the clinical response characteristics and molecular regulatory network of ovulation induction from the M cycle to the OPU day by integrating clinical index, serum proteomics, and metabolomics analysis, providing new insights into the molecular mechanisms supporting follicular development and treatment efficacy under the GnRH antagonist protocol.\u003c/p\u003e \u003cp\u003eOvarian reserve is a core indicator for assessing female fertility potential, and is crucial for selecting ART protocols and predicting treatment efficacy. AMH, a hormone specifically secreted by granulosa cells of growing follicles, directly reflects the quantity and quality of follicles in the ovary and is a globally recognized key biomarker for ovarian reserve \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The baseline AMH levels of patients enrolled in this study fell within the range of good ovarian reserve as defined by clinical consensus \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. From the perspective of endocrine dynamics, hormone levels such as FSH, E2, and P were significantly elevated during the OPU phase compared to the M phase, consistent with the physiological patterns of follicular maturation during ovulation induction. Elevated FSH levels were the core driver of follicle recruitment and dominant follicle selection \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The addition of antagonists inhibits the endogenous LH peaks, but does not affect follicle hormone synthesis function \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. E2 is synthesized by granulosa cells, and its accumulation reflects the functional activity of follicular granulosa cells, which was directly correlated with the clinical outcome of 15.3 oocytes retrieved. Previous studies have confirmed that there was a positive correlation between E2 level and the number of oocytes retrieved, with a more significant correlation in patients with good ovarian reserve \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Higher levels of P and T are associated with the initiation of corpus luteum formation and the activation of theca cell function, further supporting the synchrony and maturity of follicular development \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In addition, the physiological fluctuation of LH further supports the normal regulatory feedback of the hypothalamic-pituitary-ovarian axis, indicating that the GnRH antagonist protocol effectively triggers the synergistic activation of the endocrine network \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The final treatment outcomes showed an average of 15.3 retrieved oocytes and 5.3 retrieved embryos, fully demonstrating that the antagonist protocol used in this study has reliable clinical efficacy. These results were basically consistent with reports from similar studies at home and abroad, with the number of oocytes retrieved slightly exceeding the average level, further highlighting the effectiveness of the protocol \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring the complex physiological process of ovulation induction, dynamic changes in serum proteins serve as molecular markers responding to treatment. Throughout ovulation induction, the serum proteome exhibited significant stage-specific alterations, with more than 900 unique proteins identified in M and OPU phases, respectively. This finding suggested that the body maintained its basic physiological functions, and responded to ovulation induction by selectively remodeling. Differentially expressed proteins were significantly enriched in the cell cycle, AMPK, oocyte meiosis, and PI3K-Akt pathways, which were core regulatory pathways for follicle development and oocyte maturation. The antagonist protocol ensured follicular and oocyte development through the coordinated activation of these pathways. First, activation of the cell cycle pathway ensured the proliferation and division of granulosa cells and oocytes, providing the cellular foundation for follicle growth \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Second, the AMPK pathway, functioning as an energy metabolism switch, regulated glucose and lipid metabolism to provide energy for follicle development, thus adapting to the increased energy demands of follicles under the antagonist protocol \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Finally, the PI3K-Akt pathway participated in the initiation and resumption of oocyte meiosis, regulating oocyte maturation indirectly \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Core proteins identified via PPI analysis further revealed critical molecular nodes. Ribosomal proteins RPL23 and RPS15 participate in translation initiation, and their differential expression may be associated with the increased demand for protein synthesis during granulosa cell proliferation \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Members of the T-complex family, TCP1 and CCT2, support the folding and functional performance of meiosis-related proteins through their chaperone activity, thus indirectly regulating the meiotic process \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Peroxidase PRDX1 may maintain the redox balance of oocytes by scavenging reactive oxygen species \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Additionally, YWHAG was involved in both the cell cycle and oocyte meiosis pathways, suggesting that it may act as a cross-regulatory node to coordinate signals for cell division and maturation. These critical proteins collectively form the molecular basis for follicular development through complex interaction networks.\u003c/p\u003e \u003cp\u003eAs a technology for capturing global metabolic changes, metabolomics dynamically reflects the body\u0026rsquo;s essential demands through its own variations. Non-targeted metabolomic analysis showed significant clustering differences between M-phase and OPU-phase samples in both positive and negative ion modes. Unlike the proteome, changes in the metabolome were dominated by abundance regulation, with no stage-specific metabolites identified. This suggested that the body met the metabolic requirements of ovulation induction through the dynamic allocation of existing metabolites rather than the synthesis of new substances. This regulatory pattern could maintain the body\u0026rsquo;s metabolic homeostasis while satisfying the needs of follicular development, and it also explained from a metabolic perspective why the antagonist protocol was more effective in maintaining metabolic balance in patients compared with other protocols \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In terms of metabolite classification, lipids and lipid-like molecules accounted for the highest proportion of metabolites, which may be related to the role of lipids as energy reserves and precursors for steroid hormone synthesis during follicular development \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Moreover, ATP produced by lipid metabolism served as the main energy source for follicular development\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Under the POS mode, galactocerebroside (HMDB0249909), which showed the most significant expression change, regulated membrane fluidity by interacting with other sphingolipids, affecting sperm motility, and endometrial receptivity \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Pregnanetriol 3a-O-β-D-glucuronide (HMDB0247360) under the NEG mode, the metabolite with the most prominent difference, was a progesterone metabolite, and its high expression confirmed the initiation of corpus luteum function during the OPU phase \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The significant enrichment of differentially expressed metabolites in the steroid hormone biosynthesis pathway further reflected the metabolic mechanisms underlying the elevated sex hormone levels during the OPU phase. Glutathione, as the primary antioxidant, maintained the redox balance of oocytes through changes in its abundance, which synergized with the upregulation of PRDX1 in the proteome \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Purin metabolism, as an important intracellular metabolic pathway, provided raw materials for nucleic acid synthesis during follicular cell proliferation, jointly ensuring follicular development \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Abnormal purine metabolism could exacerbate oxidative stress, inhibit granulosa cell proliferation, and induce follicular atresia \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Finally, core metabolites identified by the random forest model, such as androstenol and oxoglutaric acid, were highly expressed in the OPU phase, suggesting that they might serve as potential markers for evaluating the efficacy of ovulation induction under the antagonist protocol.\u003c/p\u003e \u003cp\u003eLastly, the synergistic links underlying the molecular mechanism of ovulation induction by the antagonist protocol were elucidated through integrated analysis of proteins and metabolites. Spearman correlation analysis identified 192 pairs of significantly correlated protein-metabolite associations, and three core synergistic pathways were recognized, including glutathione metabolism, purine metabolism, and nucleotide sugar biosynthesis. The module centered on NUDT16 showed a negative correlation with inosine, an intermediate product of purine metabolism, suggesting that NUDT16 may maintain oocyte energy supply by regulating purine homeostasis. Inosine could either provide energy through decomposition or be converted into adenine to participate in DNA synthesis \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. The negative regulation of NUDT16 prevented the excessive inosine decomposition, ensuring the supply of nucleic acid raw materials required for follicular cell proliferation \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The strong association between GSR and glutathione, along with their co-downregulation, reflects the dynamic adjustment of antioxidant requirements during the OPU phase, activating oocyte meiosis-related signaling pathways \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The synergistic effects of these core pathways and molecules collectively formed a molecular network involving hormone synthesis, oxidative homeostasis, and energy supply. These networks explained how the antagonist protocol met the comprehensive demands of follicular development and oocyte maturation through the dynamic regulation of proteins and metabolites.\u003c/p\u003e \u003cp\u003eThis study still has certain limitations. First, the small sample size might restrict the generalizability of the results. Second, the functions of differential molecules were not verified, and some regulatory relationships required further confirmation. Additionally, no comparison was made with other stimulation protocols, so it was difficult to determine whether the molecular regulatory network identified in this study was unique to the GnRH antagonist protocol. Future research should expand the sample size and include multi-center data to verify the stability of core biomarkers. Meanwhile, cell models or animal experiments should be used to clarify the specific functions of key molecules, thereby providing more direct evidence for the elaboration of the mechanism.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, this study systematically revealed the molecular dynamic characteristics of ovulation induction during the IVF fresh cycles through integrated serum proteomic and metabolomic analyses. This study confirmed that the GnRH antagonist protocol exhibited favorable clinical efficacy in the treatment of such IVF fresh cycles. Notably, this study found that ovulation induction under this protocol regulated the dynamic changes of the serum proteome and metabolome for the first time, constructing a molecular regulatory network centered on glutathione metabolism, and purine metabolism. These networks supported follicular development and oocyte maturation through the synergistic effect of multiple pathways. Collectively, the results not only verified the clinical effectiveness of the GnRH antagonist protocol but also offered potential molecular targets and biomarkers for the optimizing follicular maturation monitoring indicators and improving of IVF treatment outcomes in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cstrong\u003eand consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the ethics committee of Shanghai Sixth People\u0026rsquo;s Hospital Affiliated to Shanghai Jiaotong University School of Medicine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll research subjects have signed informed consent forms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailable of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNil\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception: M. Tao; Data Curation: H. Shao, N. Wang; Formal analysis: TM. Ye; Methodology: M. Tao; Validation: H. Shao, N. Wang; Visualization: TM. Ye; Writing - Original Draft: TM. Ye; Writing - Review \u0026amp; Editing: M. Tao. Final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChiware, T. 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However, the molecular mechanisms remain unclear. This study aimed to analyze serum proteomic and metabolomic profiles during the menstrual cycle (M) and ovum pick up day (OPU) in patients under this protocol.\u003c/p\u003e\u003ch2\u003eDesign:\u003c/h2\u003e \u003cp\u003eTen patients undergoing the GnRH antagonist protocol were enrolled. Serum samples collected during the M and OPU phases were used for proteomics and non-targeted metabolomics analyses. Functional enrichment, protein-protein interaction (PPI), and correlation analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eClinical indicators confirmed the efficacy of the GnRH antagonist protocol, as evidenced by an anti-M\u0026uuml;llerian hormone level of 3.70 ng/ml, significantly elevated sex hormone levels at OPU, and 15.3 oocytes and 5.3 embryos retrieved. Proteomics identified 83 differentially expressed proteins enriched in cell cycle, AMPK, oocyte meiosis, and PI3K-Akt pathways, with core regulatory proteins including RPL23, TCP1, and PRDX1. Metabolomics revealed 66 differential metabolites involved in steroid hormone biosynthesis, glutathione, and purine metabolism pathways. Integrated analysis obtained 192 significantly correlated protein-metabolite pairs, emphasizing the glutathione, purine metabolism, and nucleotide sugar biosynthesis pathways. Key relationships such as NUDT16-inosine and GSR-glutathione were also identified.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study firstly revealed the dynamic changes in the serum proteome and metabolome between the M and OPU phases under the GnRH antagonist protocol. The study highlighted the role in regulating hormone synthesis, oxidative balance, and energy supply to support follicular development. These findings provide new insights and potential biomarkers for improving IVF outcomes.\u003c/p\u003e","manuscriptTitle":"Serum Proteomic and Metabolomic Profiling Uncovers Molecular Mechanisms in Patients Undergoing In-Vitro Fertilization with the GnRH Antagonist Protocol","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 00:34:05","doi":"10.21203/rs.3.rs-7779262/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1df35356-05b2-4201-8200-7996dc89305b","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62503895,"name":"Biological sciences/Biochemistry"},{"id":62503896,"name":"Health sciences/Biomarkers"},{"id":62503897,"name":"Health sciences/Endocrinology"},{"id":62503898,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-03-02T06:10:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 00:34:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7779262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7779262","identity":"rs-7779262","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}