FMR1 regulates immune-related gene expression and alternative splicing in human granulosa cells

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However, the ovarian-specific molecular mechanisms by which FMR1 dysregulation contributes to ovarian dysfunction remain poorly understood. In particular, the roles of FMR1 in coordinating gene expression and alternative splicing in granulosa cells have not been systematically explored. Methods Using lentiviral-mediated overexpression, we established stable FMR1-expressing KGN cell lines alongside wild-type controls, subsequently interrogating their transcriptomes through bulk RNA sequencing. Differential expressed genes were assessed using edgeR, followed by functional annotation using KOBAS 2.0 for Gene Ontology (GO) biological processes and KEGG pathway enrichment analyses. Alternative splicing events (ASEs) were identified with the ABLas pipeline. Selected genes and splicing events were validated by RT-qPCR. Results FMR1 overexpression induced substantial transcriptional reprogramming, with 3,008 differentially expressed genes identified—1,531 showing increased abundance, 1,477 decreased. Upregulated genes were predominantly enriched in type I interferon signaling and antiviral immune response pathways, whereas downregulated genes were mainly associated with cholesterol biosynthesis and steroid metabolic processes. RT-qPCR confirmed increased expression of BMP4, IRF7, IFI27, FAT1 , and PLAU , together with reduced expression of FABP3 and CSF1 (all P < 0.001). In parallel, FMR1 overexpression led to widespread alterations in alternative splicing across 4,077 genes, enriched for translation machinery, RNA processing, and splicing regulation. Intersection analysis identified 208 genes exhibiting coordinated changes in both expression levels and splicing patterns, with significant enrichment in innate immunity and cellular metabolism. RT-qPCR further confirmed significant splicing in four targets: IFI27 demonstrated reduced splice-form ratios (P < 0.001), while LAMC2 , IRF7 , and CSF1 exhibited elevated ratios (P < 0.05). Conclusion These findings reveal that FMR1 regulates immune-related and metabolic gene networks in human granulosa cells through coordinated modulation of gene expression and alternative splicing. This dual-layer regulatory effect provides mechanistic insight into how FMR1 dysregulation may contribute to ovarian dysfunction and FXPOI pathogenesis, and highlights alternative splicing as an important but previously underappreciated regulatory mechanism in ovarian biology. FMR1 premature ovarian insufficiency alternative splicing post-transcriptional regulation immune-related pathways granulosa cells RNA sequencing Figures Figure 1 Figure 2 Figure 3 Introduction Premature ovarian insufficiency (POI) is defined by the loss of normal ovarian function before age 40, affecting approximately 1% of women worldwide. Contemporary diagnostic frameworks mandate both (i) amenorrhea or oligomenorrhea persisting for at least four months minimum and (ii) elevated serum follicle-stimulating hormone (FSH) ≥ 25 IU/L documented on two separate occasions separated by a minimum four-week interval( 1 , 2 ). Beyond reproductive dysfunction, POI frequently imposes substantial psychological morbidity, manifesting as anxiety disorders and depression. Although etiological factors span genetic aberrations, autoimmune processes, metabolic perturbations, toxic exposures, infectious agents, and iatrogenic injury, the vast majority of cases lack an identifiable cause and therefore classified as idiopathic( 1 – 3 ). Within the spectrum of recognized genetic contributors, premutation expansions in the fragile X mental retardation 1 ( FMR1 ) gene represent the predominant monogenic cause of POI( 4 ). Located at chromosomal position Xq27.3, FMR1 encompasses approximately 40 kilobases distributed across 17 exons. Exon 1 harbors a polymorphic CGG trinucleotide repeat tract within its 5’ untranslated region (UTR)( 5 ). Alleles containing fewer than 45 CGG repeats constitute the normal range, whereas premutation alleles contain 55–200 repeats. Female premutation carriers face a markedly increased risk of POI, manifesting as fragile X-associated POI (FXPOI), with prevalence estimates ranging from 13–26% ( 6 – 8 ). These epidemiological observations underscore the critical importance of elucidating the molecular mechanisms linking FMR1 dysregulation to ovarian dysfunction. The FMR1 locus encodes the fragile X mental retardation protein (FMRP), an RNA-binding protein expressed in neuronal tissue as well as in ovarian granulosa cells ( 9 , 10 ). FMRP primarily functions in regulating mRNA metabolic fate, including stability, subcellular localization, and translational efficiency—in part through ribosomal stalling at specific target transcripts ( 11 – 13 ). Within ovarian tissue, FMR1 appears to play a role in primordial follicle activation and follicular reserve maintenance, potentially through PI3K/AKT/mTOR signaling pathways and autophagic processes in granulosa cells ( 9 , 10 ). More recent investigations have demonstrated previously unappreciated nuclear functions of FMRP, including participation in pre-mRNA alternative splicing regulation ( 14 – 16 ). Alternative splicing constitutes a pivotal post-transcriptional mechanism expanding protein diversity and enabling precise regulation of cellular identity and functional specialization ( 17 , 18 ). FMR1 itself undergoes extensive alternative splicing, yielding up to 12 distinct transcript isoforms. Intriguingly, FMRP binding sites within FMR1 pre-mRNA act as splicing enhancers, contributing to autoregulatory control of its own transcript processing ( 15 , 16 ). Beyond auto-regulation, FMRP modulates splicing of additional genes, particularly those containing G-quartet RNA secondary structures (14, 16, 19). Despite these advances, the pathogenic mechanisms underlying FXPOI have yet to be fully elucidated. The prevailing “RNA toxicity” hypothesis suggests that expanded CGG-repeat FMR1 transcripts form intranuclear inclusions, sequestering RNA-binding proteins and components of the splicing machinery, thereby disrupting normal RNA processing ( 20 ). However, a systematic investigation of how FMR1 coordinates transcriptional regulation and alternative splicing to influence ovarian function has not yet been undertaken. In the present investigation, we sought to systematically characterize transcriptomic and alternative splicing landscapes governed by FMR1 in human granulosa cells. Employing an FMR1 overexpression model in the KGN cell line coupled with RNA sequencing and experimental validation, we discovered that FMR1 modulates immune-related gene expression programs while simultaneously reshaping alternative splicing patterns. These findings provide mechanistic insights into FXPOI pathogenesis and highlight coordinated transcriptional and post-transcriptional regulation as a potential contributor to ovarian dysfunction. Materials and Methods FMR1 Cloning and Plasmid Construction To establish stable FMR1 overexpression, we employed lentiviral-mediated gene delivery (GenePharma, Suzhou, China). The complete human FMR1 coding sequence (NM_002024.6) was cloned into the LV5 lentiviral vector (EF-1α/GFP/Puro/Amp), allowing constitutive transgene expression, fluorescence-based monitoring of infection efficiency, and puromycin-based antibiotic selection. Cell Culture and Viral Infection The human granulosa-like tumor cell line KGN ( 21 ) was obtained from Procell Life Science & Technology Co., Ltd. (China). Cells were cultivated in DMEM/F12 medium (Procell, PM150312) supplemented with 10% fetal bovine serum (Hyclone, SH30084.03), 100 U/mL penicillin, and 100 µg/mL streptomycin under standard culture conditions (37°C, 5% CO₂, and saturated humidity). Mycoplasma contamination was routinely monitored and excluded. For lentiviral transduction, KGN cells were seeded in six-well plates at density of 1×10⁵ cells per well. Lentiviral particles (LV5-FMR1 or empty LV5-NC vector) were added at a multiplicity of infection (MOI) of 100. Following 72–96 hours of incubation, infection efficiency was assessed by fluorescence microscopy. Cells were harvested 48 hours after confirmation of successful transduction for downstream molecular analyses. RNA Extraction and RNA Sequencing Total RNA isolation utilized TRIzol reagent (Invitrogen, 15596026) following the acid guanidinium thiocyanate-phenol-chloroform extraction method ( 22 ). RNA quality assessment employed NanoDrop™ One spectrophotometry and Qubit 3.0 fluorometry, and RNA integrity was further evaluated by agarose gel electrophoresis. Stranded RNA-seq libraries were constructed from 2 µg total RNA using the KC™ Stranded mRNA Library Prep Kit (Seqhealth, Wuhan, China). Sequencing was conducted on an Illumina NovaSeq 6000 platform (150 bp paired-end reads). Following adapter trimming and quality filtering, clean reads underwent alignment to the human reference genome using HISAT2 ( 23 ). Transcript quantification employed FPKM (fragments per kilobase of transcript per million mapped reads) normalization. Transcriptomic and Splicing Analysis Differential gene expression analysis utilized edgeR ( 24 ), applying statistical thresholds of adjusted P < 0.05 and |fold change| ≥ 2. Alternative splicing event (ASE) were identified and quantified using the ABLas pipeline, as previously described ( 25 ), which classifies ten splicing event types based on splice junction reads. Statistical significance was assessed via Fisher’s exact test. Regulated ASEs (RASEs) were defined by |ΔPSI| ≥ 0.1 and adjusted P < 0.05; genes harboring RASEs were designated regulated alternative splicing genes (RASGs). Functional Enrichment Analysis Gene Ontology (GO) biological process and KEGG pathway enrichment analyses were conducted using KOBAS 2.0 ( 26 ), with Benjamini-Hochberg false discovery rate (FDR) correction. Enriched terms satisfied FDR < 0.05. RT-qPCR Validation Total RNA reverse transcription employed the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). Quantitative PCR was performed using SYBR Green Master Mix on a QuantStudio Real-Time PCR System. Gene expression levels were normalized to GAPDH and quantified using the 2^-ΔΔCt method ( 27 ). Primer sequences are provided in Supplementary Table S1 . Alternative splicing validation targeted specific isoforms using isoform-specific primer pairs designed to flank relevant splice junctions. Statistical Analysis Data are presented as mean ± standard deviation (SD) from three independent biological replicates. Statistical comparisons employed Student’s t-test. P < 0.05 was considered statistically significant. All analyses utilized R software (version 4.0.3) and GraphPad Prism 9. Results FMR1 Overexpression Reshapes Global Transcriptional Profiles RNA sequencing comparison between FMR1-overexpressing and control KGN cells identified 3,008 differentially expressed genes (DEGs) satisfying |fold change| ≥ 2 and adjusted P < 0.05 criteria. Among these, 1,531 genes exhibited increased expression, while 1,477 displayed decreased expression in FMR1-overexpressing cells (Fig. 1D-E). Principal component analysis (PCA) based on FPKM values demonstrated clear separation between FMR1-overexpressing and control samples (Fig. 1C). Volcano plot visualization (Fig. 1D) illustrated extensive transcriptional changes associated with FMR1 overexpression. RT-qPCR results comparing control and FMR1-overexpressing samples. Error bars represent mean ± SEM. ***P < 0.001. (B) Western blot analysis confirming successful FMR1 overexpression. (C) Principal component analysis (PCA) based on FPKM values of all detected genes in FMR1-overexpressing cells. The ellipse for each group represents the confidence ellipse. (D) Volcano plot displaying all differentially expressed genes (DEGs) between overexpression (OE) and control (Ctrl) samples. (E) Hierarchical clustering heatmap showing expression levels of all DEGs. Functional Enrichment Reveals Immune Activation and Metabolic Suppression GO biological process enrichment analysis of upregulated genes revealed significant enrichment of pathways related to the type I interferon signaling pathway, defense response to virus, innate immune response, and inflammatory response (Fig. 2A, left panel). KEGG pathway analysis supported these observations, identifying enrichment in influenza A, measles, herpes simplex infection, and cytokine-cytokine receptor interaction pathways (Supplementary Figure S1). Conversely, downregulated genes were primarily enriched in cholesterol biosynthetic process, steroid metabolic process, lipid biosynthetic process, and cellular response to oxidative stress (Fig. 2A, right panel). KEGG analysis further highlighted enrichment in steroid biosynthesis, terpenoid backbone biosynthesis, and metabolic pathways (Supplementary Figure S1). Collectively, these patterns indicate FMR1 overexpression is associated with enhanced immune-related activity and reduced steroid and lipid biosynthetic capacity in granulosa cells. Supplementary Figure S1. KEGG pathway enrichment of FMR1-regulated DEGs. Bar plot exhibiting the top 10 most enriched KEGG pathways for upregulated (left panel) and downregulated (right panel) DEGs. RT-qPCR Validates Expression Changes in Key Immune and Metabolic Genes We selected seven representative genes spanning distinct functional categories for RT-qPCR validation. Among upregulated genes, BMP4, IRF7, IFI27, FAT1, and PLAU all demonstrated significantly increased expression in FMR1-overexpressing cells compared with controls (all P < 0.001, Fig. 2B). Downregulated genes FABP3 and CSF1 exhibited significantly decreased expression (both P < 0.001, Fig. 2C). These validation results were strongly consistent with RNA-seq data. Scatter plot exhibiting the most enriched GO biological process results of upregulated (left panel) and downregulated (right panel) DEGs. (B) Bar plot showing expression patterns and statistical differences for selected upregulated genes. Error bars represent mean ± SEM. ***P < 0.001. (C) Bar plot showing expression patterns and statistical differences for selected downregulated genes. Error bars represent mean ± SEM. ***P < 0.001. FMR1 Overexpression Extensively Remodels the Alternative Splicing Landscape Beyond transcriptional regulation, we next investigated the impact of FMR1 overexpression on alternative splicing. ABLas pipeline analysis identified 2,300 regulated alternative splicing genes (RASGs) and 5,234 regulated alternative splicing events (RASEs) in response to FMR1 overexpression, spanning ten event types (Fig. 3A). The most prevalent event type was exon skipping (SE), followed by alternative 5′ splice site (A5SS) and alternative 3′ splice site (A3SS) events. This distribution is consistent with established alternative splicing patterns observed in mammalian cells. Intersection Analysis Identifies Genes Subject to Dual-Layer Regulation To identify genes subject to both transcriptional and post-transcriptional regulation by FMR1, we conducted an intersection analysis between DEGs and RASGs, identifying 208 genes (Fig. 3B). Go enrichment analysis of these dual-regulated genes highlighted innate immune response, defense response to virus, type I interferon signaling pathway, as well as lipid metabolic process and related metabolic pathways as the most significantly enriched biological processes (Fig. 3C). These findings indicate that FMR1 coordinates immune-related gene regulation and influences metabolic processes through both expression modulation and alternative splicing. Bar plot showing the statistical distribution of FMR1-regulated alternative splicing events (RASEs) by event type. (B) Venn diagram showing 208 overlapping genes between 2,300 RASGs (regulated alternative splicing genes) and 3,008 DEGs (differentially expressed genes), indicating dual-layer regulation by FMR1. (C) Scatter plot exhibiting the most enriched GO biological process results of the 208 overlapping genes between RASGs and DEGs, highlighting enrichment in innate immune response, response to virus, and immune system processes. (D) FMR1 regulates alternative splicing of CSF1. Left panel: IGV-sashimi plot showing regulated alternative splicing events and binding sites across mRNA. Reads distribution of RASE is plotted in the upper panel and transcripts are shown below. Right panel: Schematic diagrams depicting ASE structures with RT-qPCR validation shown at bottom. Error bars represent mean ± SEM. ***P < 0.001, **P < 0.01, *P < 0.05. (E) FMR1 regulates alternative splicing of IRF7. Layout and annotations as in panel D. RT-qPCR Validates Alternative Splicing Changes We validated alternative splicing changes in four genes using isoform-specific RT-qPCR assays. IFI27 showed a significantly decreased splicing ratio in FMR1-overexpressing cells (P < 0.001, Supplementary Figure S2B). Conversely, LAMC2, IRF7, and CSF1 all exhibited significantly increased splicing ratios (P < 0.05, Fig. 3D–E and Supplementary Figure S2A). Of particular interest, LAMC2 displayed altered splicing without significant changes in overall gene expression, indicating that FMR1 can regulate transcript isoform composition independently of transcript abundance. Supplementary Figure S2. FMR1 regulates LAMC2 and IFI27 splicing. FMR1 regulates alternative splicing of LAMC2. Left panel: IGV-sashimi plot showing regulated alternative splicing events and binding sites across mRNA. Reads distribution of RASE is plotted in the upper panel and transcripts are shown below. Right panel: Schematic diagrams depicting ASE structures with RT-qPCR validation shown at bottom. Error bars represent mean ± SEM. ***P < 0.001, **P < 0.01, *P < 0.05. (B) FMR1 regulates alternative splicing of IFI27. Layout and annotations as in panel A. Discussion Principal Findings and Biological Implications This investigation identifies FMR1 as a central regulatory node coordinating transcriptional and post-transcriptional programs in human granulosa cells. Three principal findings emerge: ( 1 ) FMR1 overexpression is associated with activation of immune-related pathways accompanied by suppression of steroidogenic processes, ( 2 ) FMR1 exerts broad effects on the alternative splicing landscape, particularly affecting RNA processing machinery, and ( 3 ) 208 genes undergo coordinated dual-layered regulation, experiencing coordinated changes in both expression levels and isoform composition. To our knowledge, this research is the first to systematically elucidate that FMR1 coordinates immune-related transcriptional programs and alternative splicing regulation in human granulosa cells, establishing a dual-layer regulatory framework relevant to FXPOI. FMR1’s Role in Granulosa Cell Immune Function The pronounced enrichment of upregulated genes in type I interferon signaling and antiviral defense pathways suggests FMR1 may shift granulosa cells toward a heightened immune-surveillance-like state. This observation aligns with growing evidence implicating inflammatory signaling in ovarian aging and follicular attrition( 28 , 29 ). Key upregulated immune mediators warrant specific discussion. IRF7, encoding interferon regulatory factor 7, serves as a master transcriptional activator of type I interferon responses ( 30 ). Persistent IRF7 activation has been linked to autoimmune disease pathogenesis ( 31 ). Additionally, IRF7 itself undergoes extensive alternative splicing, generating isoforms with distinct functional properties in viral defense ( 32 , 33 ). Our finding that IRF7 exhibits both elevated expression and altered splicing suggests a multilayered mode of immune regulation by FMR1. IFI27 (interferon alpha-inducible protein 27) represents another interferon-stimulated gene showing dual regulation. Recent investigations demonstrate IFI27’s complex immunomodulatory functions, including both positive and negative regulation of antiviral signaling depending on cellular context( 34 – 36 ). The combination of increased IFI27 expression but decreased splicing ratio in our data raises the possibility of isoform-specific functional divergence, rather than uniform amplification of IFI27 activity. The autoimmune component of POI pathogenesis is well-established ( 4 ). Chronic immune activation in granulosa cells could generate inflammatory microenvironments detrimental to follicular development and survival. Whether FMR1-driven immune activation represents an adaptive response to cellular stress or maladaptive process contributing to follicular depletion remains an important question for future investigation. FMR1’s Impact on Metabolic Homeostasis Downregulation of cholesterol biosynthesis and steroid metabolism genes presents another critical finding. FABP3 (fatty acid binding protein 3), regulator of fatty acid transport and PPARα signaling, plays essential roles in lipid trafficking and energy metabolism ( 37 ). CSF1 (colony stimulating factor 1), while primarily known for immune cell regulation, has also been implicated in systemic metabolic regulation, including the mobilization of adipose fat and modulation of hepatic glucose uptake through effects on macrophage populations ( 38 ). Steroid hormone synthesis depends critically on cholesterol as substrate. Impaired steroidogenic capacity could compromise granulosa cell endocrine function, contributing to follicular dysfunction. The inverse relationship between immune activation and metabolic function suggests that FMR1 dysregulation may force granulosa cells into functional trade-offs between immune surveillance and reproductive competence, a phenomenon observed across evolutionary biology. These findings provide a mechanistic explanation for the steroidogenic suppression highlighted in the Abstract and support a functional link between FMR1 dysregulation and impaired granulosa cell endocrine capacity. Alternative Splicing as a Regulatory Layer The identification of 4,077 genes undergoing FMR1-regulated alternative splicing highlights the magnitude of post-transcriptional control exerted by this gene. Enrichment of RNA splicing, translation, and protein folding processes suggests that FMR1 influences not only individual target genes but also the core gene expression machinery itself, potentially establishing regulatory feedback circuits. FMRP’s nuclear functions in splicing regulation are increasingly recognized( 39 ). Our data demonstrate that his regulatroy role extends beyond neuronal tissue to granulosa cells, with potential relevance to FXPOI pathogenesis. The RNA toxicity hypothesis suggests CGG-repeat expansions sequester splicing factors ( 40 ); our findings provide functional support for this model by revealing widespread splicing perturbations associated with FMR1 dysregulation. The subset of 208 dual-regulated genes represents a particularly important regulatory layer. These genes experience coordinated control at both transcriptional and post-transcriptional levels, suggesting they may constitute core functional targets of FMR1-mediated regulation. Their enrichment in immune processes reinforces the central role of immune dysregulation in FMR1-associated ovarian dysfunction. Our studies expand the functional scope of FMR1 beyond translational control, positioning alternative splicing dysregulation as a central and previously underappreciated mechanism contributing to ovarian dysfunction. LAMC2 as an Example of Pure Splicing Regulation LAMC2 (laminin subunit gamma 2) provides an instructive example of post-transcriptional regulation independent of transcript abundance. This gene exhibited significantly altered splicing ratios without accompanying expression changes, exemplifying alternative splicing as an independent regulatory mechanism. Laminins constitute essential extracellular matrix components influencing cell adhesion, migration, and signaling ( 41 , 42 ). Distinct LAMC2 isoforms likely possess differential functional properties relevant to granulosa cell-extracellular matrix interactions critical for folliculogenesis. This observation underscores that analyses focused exclusively on expression levels would overlook substantial regulatory complexity. Alternative splicing expands proteomic diversity and functional adaptability beyond what transcript abundance alone can capture. Clinical and Translational Implications These molecular insights carry potential translational significance for FXPOI management. First, immune-related genes exhibiting dual regulation (IRF7, IFI27, CSF1) represent candidate biomarkers for POI risk stratification among premutation carriers. Second, the immune activation signature observed immunomodulatory interventions may merit exploration as fertility-preserving interventions, although careful consideration of immune system complexity and context-dependence is essential. Third, identification of specific splice isoforms altered by FMR1 dysregulation raises the possibility of splice‑switching oligonucleotide‑based therapies, an approach that has shown therapeutic promise by correcting aberrant splicing and restoring functional protein expression in genetic disorders such as fragile X syndrome ( 43 , 44 ). Targeting pathological splice variants while preserving physiological isoforms could enable precision therapeutic strategies. Fourth, metabolic support approaches aimed at restoring steroidogenic function could supplement immune-targeted approaches. However, clinical trials evaluating metabolic supplements, such as coenzyme Q10, in diminished ovarian reserve have yielded mixed results( 45 ), suggesting that metabolic correction alone may be insufficient. Mechanistic Insights Into FXPOI Pathogenesis Our findings provide mechanistic support for the RNA toxicity hypothesis while extending its scope. If CGG-repeat expansions sequester splicing regulators, the resulting splicing dysregulation could manifest as widespread isoform imbalances, particularly affecting immune and metabolic pathways critical for ovarian homeostasis. The combination of immune hyperactivation and metabolic dysfunction could create conditions incompatible with sustained folliculogenesis. An alternative interpretation is that some observed changes represent compensatory rather than primary pathogenic responses. Immune activation may reflect granulosa cells’ attempts to mitigate cellular stress imposed by FMR1 dysregulation. Distinguishing adaptive from maladaptive responses will require time-resolved and functional studies, which cannot be addressed by the current cross-sectional design. Comparison With Existing Literature Our findings align with and extend previous work demonstrating FMR1’s roles in follicle activation via PI3K/AKT/mTOR signaling and autophagy regulation( 9 , 10 ). We add an additional regulatory dimension—immune signaling and alternative splicing—to FMR1’s ovarian functions. Recent studies documented extensive FMRP-regulated splicing in neurons ( 19 , 39 ), whereas ovarian splicing regulation by FMR1 has remained largely unexplored. Our data demonstrate this regulatory function extends to granulosa cells, with functional consequences potentially relevant to FXPOI. Single-cell transcriptomic analyses of POI patient ovaries revealed altered immune cell populations and granulosa cell-immune cell interactions ( 46 ). Our findings provide molecular mechanisms that may underlie these cellular-level observations. Future Research Directions Several research avenues emerge from these findings: Model system validation : Findings should be validated in primary human granulosa cells and patient-derived iPSCs. Engineering CGG repeat expansions into model systems would better recapitulate FXPOI molecular pathology than overexpression alone. Longitudinal studies : Prospective molecular profiling of young premutation carriers prior to POI onset may enable early biomarkers discovery. Mechanistic investigations : ChIP-seq, RIP-seq, and loss-of-function experiments are needed to establish causal relationships and determine the functional consequences of specific splice isoforms. Intervention studies : Testing immune-modulatory or splicing-corrective strategies in animal models could provide preclinical evidence for therapeutic development. Study Limitations Several limitations warrant consideration. First, KGN cells, while widely used, originate from a granulosa cell tumor and may not fully recapitulate normal physiology. Second, overexpression models differ from premutation biology involving CGG expansions. Third, transcriptomic analyses provide correlative rather than causal insights. Fourth, only a single post-transduction timepoint was examined. Finally, in vivo validation remains necessary to confirm relevance in whole-organism contexts. Conclusion This investigation identifies FMR1 as a central coordinator of dual-layered programs in human granulosa cells. Through simultaneous modulation of transcript abundance and alternative splicing, FMR1 profoundly influences immune signaling, metabolic homeostasis, and cellular stress responses—all critical determinants of ovarian function. Together, these findings extend current models of FXPOI by integrating immune dysregulation and alternative splicing defects as convergent downstream consequences of FMR1 dysfunction. The immune dysregulation and metabolic impairment observed offer mechanistic insight to FXPOI pathogenesis, while widespread splicing alterations reveal previously unrecognized regulatory complexity. The 208 genes to coordinated transcriptional and post-transcriptional control represent a core FMR1-controlled network, enriched in immune pathways, and likely critical for ovarian homeostasis. The identification of specific genes (IRF7, IFI27, FAT1, LAMC2, CSF1) exhibiting coordinated regulation provides concrete targets for biomarker development and therapeutic intervention. Translating these cellular insights into clinical strategies may ultimately improve fertility preservation and quality of life for women carrying FMR1 premutation alleles. Abbreviations ASEs, alternative splicing events; DEG, differentially expressed gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NC, negative control; OE, overexpression; POI, premature ovarian insufficiency; FXPOI, fragile X–associated primary ovarian insufficiency; RASEs, regulated alternative splicing events; RASGs, regulated alternative splicing genes; RBPs, RNA-binding proteins; FMRP, fragile X mental retardation protein Declarations Competing Interests The authors declare no competing interests. Ethics Statement All experiments were performed in accordance with institutional guidelines. The KGN cell line was obtained from commercial sources with appropriate documentation. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Conceptualization: Lan Chao, Shuo Zhao.Methodology: Shuo Zhao, Dong Hou, Chengzi Huang.Formal analysis: Shuo Zhao, Shan Dong, Yikai Chen.Investigation: Shuo Zhao, Dong Hou, Chengzi Huang, Shasha Qi.Data curation: Shan Dong, Lili Wang.Visualization: Shuo Zhao, Yikai Chen.Writing – original draft: Shuo Zhao.Writing – review & editing: Lan Chao, Lili Wang.All authors read and approved the final manuscript. Acknowledgement The authors thank the staff of the sequencing platform for their technical support in RNA sequencing and data processing. We also acknowledge all members of the Center for Reproductive Medicine, Qilu Hospital of Shandong University, for helpful discussions and technical assistance. Data Availability The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2025) in National Genomics Data Center (Nucleic Acids Res 2025), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA016706) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human. References European Society for Human R, Embryology Guideline Group on POI, Webber L, Davies M, Anderson R, Bartlett J, et al. ESHRE Guideline: management of women with premature ovarian insufficiency. Hum Reprod. 2016;31(5):926–37. Eshre AC, IMSGGo POI, Panay N, Anderson RA, Bennie A, Cedars M, et al. Evidence-based guideline: premature ovarian insufficiency(dagger)(double dagger). Climacteric. 2024;27(6):510–20. Tucker EJ, Grover SR, Bachelot A, Touraine P, Sinclair AH. Premature Ovarian Insufficiency: New Perspectives on Genetic Cause and Phenotypic Spectrum. 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Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39:W316–22. Web Server issue). Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8. Isola JVV, Hense JD, Osorio CAP, Biswas S, Alberola-Ila J, Ocanas SR et al. Reproductive Ageing: Inflammation, immune cells, and cellular senescence in the aging ovary. Reproduction. 2024;168(2). Zhang Z, Schlamp F, Huang L, Clark H, Brayboy L. Inflammaging is associated with shifted macrophage ontogeny and polarization in the aging mouse ovary. Reproduction. 2020;159(3):325–37. Kirshenbaum M, Orvieto R. Premature ovarian insufficiency (POI) and autoimmunity-an update appraisal. J Assist Reprod Genet. 2019;36(11):2207–15. Wang L, Yang F, Ye J, Zhang L, Jiang X. Insight into the role of IRF7 in skin and connective tissue diseases. Exp Dermatol. 2024;33(5):e15083. Ning S, Pagano JS, Barber GN. IRF7: activation, regulation, modification and function. Genes Immun. 2011;12(6):399–414. Panthi A, Ferretti MB, Howard O, Pokharel SM, McCracken R, Boudreault S, et al. Alternatively spliced isoforms of IRF7 differentially regulate interferon expression to tune response to viral infection. Cell Rep. 2025;44(9):116166. Xiong Z, Chen P, Yuan M, Yao L, Wang Z, Liu P et al. Integrated Bioinformatics and Validation Reveal IFI27 and Its Related Molecules as Potential Identifying Genes in Liver Cirrhosis. Biomolecules. 2023;14(1). Rivero V, Carrion-Cruz J, Lopez-Garcia D, DeDiego ML. The IFN-induced protein IFI27 binds MDA5 and counteracts its activation after SARS-CoV-2 infection. Front Cell Infect Microbiol. 2024;14:1470924. Lopez-Garcia D, Rivero V, Villamayor L, DeDiego ML. IFN alpha inducible protein 27 (IFI27) acts as a positive regulator of PACT-dependent PKR activation after RNA virus infections. PLoS Pathog. 2025;21(6):e1013246. Zhuang L, Mao Y, Liu Z, Li C, Jin Q, Lu L, et al. FABP3 Deficiency Exacerbates Metabolic Derangement in Cardiac Hypertrophy and Heart Failure via PPARalpha Pathway. Front Cardiovasc Med. 2021;8:722908. Keshvari S, Masson JJR, Ferrari-Cestari M, Bodea LG, Nooru-Mohamed F, Tse BWC, et al. Reversible expansion of tissue macrophages in response to macrophage colony-stimulating factor (CSF1) transforms systemic lipid and carbohydrate metabolism. Am J Physiol Endocrinol Metab. 2024;326(2):E149–65. Jung S, Shah S, Han G, Richter JD. FMRP deficiency leads to multifactorial dysregulation of splicing and mislocalization of MBNL1 to the cytoplasm. PLoS Biol. 2023;21(12):e3002417. Rosario R, Stewart HL, Choudhury NR, Michlewski G, Charlet-Berguerand N, Anderson RA. Evidence for a fragile X messenger ribonucleoprotein 1 (FMR1) mRNA gain-of-function toxicity mechanism contributing to the pathogenesis of fragile X-associated premature ovarian insufficiency. FASEB J. 2022;36(11):e22612. Nonnast E, Mira E, Manes S. The role of laminins in cancer pathobiology: a comprehensive review. J Transl Med. 2025;23(1):83. Wang D, Keyoumu K, Yu R, Wen D, Jiang H, Liu X, et al. Extracellular matrix marker LAMC2 targets ZEB1 to promote TNBC malignancy via up-regulating CD44/STAT3 signaling pathway. Mol Med. 2024;30(1):61. Shah S, Sharp KJ, Raju Ponny S, Lee J, Watts JK, Berry-Kravis E, et al. Antisense oligonucleotide rescue of CGG expansion-dependent FMR1 mis-splicing in fragile X syndrome restores FMRP. Proc Natl Acad Sci U S A. 2023;120(27):e2302534120. Jung S, Richter JD. Trinucleotide repeat expansion and RNA dysregulation in fragile X syndrome: emerging therapeutic approaches. RNA. 2025;31(3):307–13. Lin G, Li X, Jin Yie SL, Xu L. Clinical evidence of coenzyme Q10 pretreatment for women with diminished ovarian reserve undergoing IVF/ICSI: a systematic review and meta-analysis. Ann Med. 2024;56(1):2389469. Han Y, Diao J, Wang X, Zhang S, Yuan L, Ping Y, et al. Single-cell RNA sequencing reveals common interactions between follicle immune cells and granulosa cells in premature ovarian insufficiency patientsdagger. Biol Reprod. 2025;112(1):156–68. Zhang S, Chen X, Jin E, Wang A, Chen T, Zhang X, et al. The GSA Family in 2025: A Broadened Sharing Platform for Multi-Omics and Multimodal Data. Genomics Proteomics Bioinformatics; 2025. Additional Declarations No competing interests reported. Supplementary Files Figureforjournal1.19.docx SupplementaryFullLengthWB.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8654777","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596254781,"identity":"31f7502c-4841-473b-895f-73555ddc883e","order_by":0,"name":"Shuo Zhao","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Zhao","suffix":""},{"id":596254782,"identity":"8dca55fb-269b-4a7c-9aab-4338cd186375","order_by":1,"name":"Dong Hou","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Hou","suffix":""},{"id":596254783,"identity":"10f5af02-592d-41b7-8e4b-8452ad0b3c20","order_by":2,"name":"Chengzi Huang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Chengzi","middleName":"","lastName":"Huang","suffix":""},{"id":596254784,"identity":"1a2660cb-94e5-4797-a779-bbe88d870897","order_by":3,"name":"Shan Dong","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Dong","suffix":""},{"id":596254785,"identity":"76f3d183-797e-44c6-be85-e88075df1ec4","order_by":4,"name":"Lili Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Wang","suffix":""},{"id":596254786,"identity":"222f90bf-617b-4c31-a387-b6462cd51d3e","order_by":5,"name":"Yikai Chen","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yikai","middleName":"","lastName":"Chen","suffix":""},{"id":596254787,"identity":"ebfb07a3-de11-41f6-9c95-2052a434aab4","order_by":6,"name":"Shasha Qi","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shasha","middleName":"","lastName":"Qi","suffix":""},{"id":596254788,"identity":"48c32ded-e955-4ac1-86a6-488838300465","order_by":7,"name":"Lan Chao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYDACCSB+wMAgB2IfADIYG6CC+LUkMDAYg7UkkKIlEaSSgSgt/LObHz5IqLmT3i92+CHQljrZDQeYD97mYbDLw2nJnWPGBgnHnuXOnJ1mANRy2HjDAbZkax6G5GJcWgwkEswkEtgO5264nQDSciBxwwEeM2keIKMBp5b0bxIJ/w6n299O/wByGFAL/zcCWnLMJBLbDicYSOeAbGEG2cKGV4vEjZxig8S+w4YzbucUHEgwOGw88zCbseUcg2ScWvhnpG988OHbYXn+2embP3yoqJPtO9788MabCjucWtDdCcTMMMYoGAWjYBSMArIBAK+FXUkQC04BAAAAAElFTkSuQmCC","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Lan","middleName":"","lastName":"Chao","suffix":""}],"badges":[],"createdAt":"2026-01-21 03:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8654777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8654777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103416556,"identity":"109b7c07-a66b-4513-a9e8-3bb9c65d37d6","added_by":"auto","created_at":"2026-02-25 12:12:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":671717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1. FMR1 regulates gene expression in KGN cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) RT-qPCR results comparing control and FMR1-overexpressing samples. Error bars represent mean ± SEM. ***P \u0026lt; 0.001. (B) Western blot analysis confirming successful FMR1 overexpression. (C) Principal component analysis (PCA) based on FPKM values of all detected genes in FMR1-overexpressing cells. The ellipse for each group represents the confidence ellipse. (D) Volcano plot displaying all differentially expressed genes (DEGs) between overexpression (OE) and control (Ctrl) samples. (E) Hierarchical clustering heatmap showing expression levels of all DEGs.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8654777/v1/c99feef649ea12967ef4a120.png"},{"id":103416533,"identity":"b5dbe009-6db3-4020-aacb-e4d956acf0e2","added_by":"auto","created_at":"2026-02-25 12:12:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2. Validation of FMR1-regulated DEGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scatter plot exhibiting the most enriched GO biological process results of upregulated (left panel) and downregulated (right panel) DEGs. (B) Bar plot showing expression patterns and statistical differences for selected upregulated genes. Error bars represent mean ± SEM. ***P \u0026lt; 0.001. (C) Bar plot showing expression patterns and statistical differences for selected downregulated genes. Error bars represent mean ± SEM. ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8654777/v1/56038ba727020bb3d15f4962.png"},{"id":103416612,"identity":"0b48c58a-4fac-44bd-b558-42244eef11ae","added_by":"auto","created_at":"2026-02-25 12:12:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3. FMR1 regulates alternative splicing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Bar plot showing the statistical distribution of FMR1-regulated alternative splicing events (RASEs) by event type. (B) Venn diagram showing 208 overlapping genes between 2,300 RASGs (regulated alternative splicing genes) and 3,008 DEGs (differentially expressed genes), indicating dual-layer regulation by FMR1. (C) Scatter plot exhibiting the most enriched GO biological process results of the 208 overlapping genes between RASGs and DEGs, highlighting enrichment in innate immune response, response to virus, and immune system processes. (D) FMR1 regulates alternative splicing of CSF1. Left panel: IGV-sashimi plot showing regulated alternative splicing events and binding sites across mRNA. Reads distribution of RASE is plotted in the upper panel and transcripts are shown below. Right panel: Schematic diagrams depicting ASE structures with RT-qPCR validation shown at bottom. Error bars represent mean ± SEM. ***P \u0026lt; 0.001, **P \u0026lt; 0.01, *P \u0026lt; 0.05. (E) FMR1 regulates alternative splicing of IRF7. Layout and annotations as in panel D.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8654777/v1/67ee901abf522062959ce707.png"},{"id":109296410,"identity":"2182135c-845b-4e79-9589-db9dfaf33c2e","added_by":"auto","created_at":"2026-05-15 08:46:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":581386,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8654777/v1/429f20ec-17d3-4774-930e-1eae30ec99b2.pdf"},{"id":103416500,"identity":"5b5e62bb-be04-4857-8649-77588d5d3856","added_by":"auto","created_at":"2026-02-25 12:12:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5443909,"visible":true,"origin":"","legend":"","description":"","filename":"Figureforjournal1.19.docx","url":"https://assets-eu.researchsquare.com/files/rs-8654777/v1/5eccf35db795b6469a89e541.docx"},{"id":103416501,"identity":"97f64fce-89aa-4ef4-bd91-a07b6538fd98","added_by":"auto","created_at":"2026-02-25 12:12:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7681904,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFullLengthWB.docx","url":"https://assets-eu.researchsquare.com/files/rs-8654777/v1/8f84064ff891609e3d0c23c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"FMR1 regulates immune-related gene expression and alternative splicing in human granulosa cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePremature ovarian insufficiency (POI) is defined by the loss of normal ovarian function before age 40, affecting approximately 1% of women worldwide. Contemporary diagnostic frameworks mandate both (i) amenorrhea or oligomenorrhea persisting for at least four months minimum and (ii) elevated serum follicle-stimulating hormone (FSH)\u0026thinsp;\u0026ge;\u0026thinsp;25 IU/L documented on two separate occasions separated by a minimum four-week interval(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Beyond reproductive dysfunction, POI frequently imposes substantial psychological morbidity, manifesting as anxiety disorders and depression. Although etiological factors span genetic aberrations, autoimmune processes, metabolic perturbations, toxic exposures, infectious agents, and iatrogenic injury, the vast majority of cases lack an identifiable cause and therefore classified as idiopathic(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the spectrum of recognized genetic contributors, premutation expansions in the fragile X mental retardation 1 (\u003cem\u003eFMR1\u003c/em\u003e) gene represent the predominant monogenic cause of POI(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Located at chromosomal position Xq27.3, FMR1 encompasses approximately 40 kilobases distributed across 17 exons. Exon 1 harbors a polymorphic CGG trinucleotide repeat tract within its 5\u0026rsquo; untranslated region (UTR)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Alleles containing fewer than 45 CGG repeats constitute the normal range, whereas premutation alleles contain 55\u0026ndash;200 repeats. Female premutation carriers face a markedly increased risk of POI, manifesting as fragile X-associated POI (FXPOI), with prevalence estimates ranging from 13\u0026ndash;26% (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These epidemiological observations underscore the critical importance of elucidating the molecular mechanisms linking \u003cem\u003eFMR1\u003c/em\u003e dysregulation to ovarian dysfunction.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eFMR1\u003c/em\u003e locus encodes the fragile X mental retardation protein (FMRP), an RNA-binding protein expressed in neuronal tissue as well as in ovarian granulosa cells (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). FMRP primarily functions in regulating mRNA metabolic fate, including stability, subcellular localization, and translational efficiency\u0026mdash;in part through ribosomal stalling at specific target transcripts (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Within ovarian tissue, \u003cem\u003eFMR1\u003c/em\u003e appears to play a role in primordial follicle activation and follicular reserve maintenance, potentially through PI3K/AKT/mTOR signaling pathways and autophagic processes in granulosa cells (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore recent investigations have demonstrated previously unappreciated nuclear functions of FMRP, including participation in pre-mRNA alternative splicing regulation (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Alternative splicing constitutes a pivotal post-transcriptional mechanism expanding protein diversity and enabling precise regulation of cellular identity and functional specialization (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). FMR1 itself undergoes extensive alternative splicing, yielding up to 12 distinct transcript isoforms. Intriguingly, FMRP binding sites within \u003cem\u003eFMR1\u003c/em\u003e pre-mRNA act as splicing enhancers, contributing to autoregulatory control of its own transcript processing (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Beyond auto-regulation, FMRP modulates splicing of additional genes, particularly those containing G-quartet RNA secondary structures (14, 16, 19).\u003c/p\u003e \u003cp\u003eDespite these advances, the pathogenic mechanisms underlying FXPOI have yet to be fully elucidated. The prevailing \u0026ldquo;RNA toxicity\u0026rdquo; hypothesis suggests that expanded CGG-repeat \u003cem\u003eFMR1\u003c/em\u003e transcripts form intranuclear inclusions, sequestering RNA-binding proteins and components of the splicing machinery, thereby disrupting normal RNA processing (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, a systematic investigation of how \u003cem\u003eFMR1\u003c/em\u003e coordinates transcriptional regulation and alternative splicing to influence ovarian function has not yet been undertaken.\u003c/p\u003e \u003cp\u003eIn the present investigation, we sought to systematically characterize transcriptomic and alternative splicing landscapes governed by \u003cem\u003eFMR1\u003c/em\u003e in human granulosa cells. Employing an \u003cem\u003eFMR1\u003c/em\u003e overexpression model in the KGN cell line coupled with RNA sequencing and experimental validation, we discovered that \u003cem\u003eFMR1\u003c/em\u003e modulates immune-related gene expression programs while simultaneously reshaping alternative splicing patterns. These findings provide mechanistic insights into FXPOI pathogenesis and highlight coordinated transcriptional and post-transcriptional regulation as a potential contributor to ovarian dysfunction.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFMR1 Cloning and Plasmid Construction\u003c/h2\u003e \u003cp\u003eTo establish stable FMR1 overexpression, we employed lentiviral-mediated gene delivery (GenePharma, Suzhou, China). The complete human FMR1 coding sequence (NM_002024.6) was cloned into the LV5 lentiviral vector (EF-1α/GFP/Puro/Amp), allowing constitutive transgene expression, fluorescence-based monitoring of infection efficiency, and puromycin-based antibiotic selection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell Culture and Viral Infection\u003c/h3\u003e\n\u003cp\u003eThe human granulosa-like tumor cell line KGN (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) was obtained from Procell Life Science \u0026amp; Technology Co., Ltd. (China). Cells were cultivated in DMEM/F12 medium (Procell, PM150312) supplemented with 10% fetal bovine serum (Hyclone, SH30084.03), 100 U/mL penicillin, and 100 \u0026micro;g/mL streptomycin under standard culture conditions (37\u0026deg;C, 5% CO₂, and saturated humidity). Mycoplasma contamination was routinely monitored and excluded.\u003c/p\u003e \u003cp\u003eFor lentiviral transduction, KGN cells were seeded in six-well plates at density of 1\u0026times;10⁵ cells per well. Lentiviral particles (LV5-FMR1 or empty LV5-NC vector) were added at a multiplicity of infection (MOI) of 100. Following 72\u0026ndash;96 hours of incubation, infection efficiency was assessed by fluorescence microscopy. Cells were harvested 48 hours after confirmation of successful transduction for downstream molecular analyses.\u003c/p\u003e\n\u003ch3\u003eRNA Extraction and RNA Sequencing\u003c/h3\u003e\n\u003cp\u003eTotal RNA isolation utilized TRIzol reagent (Invitrogen, 15596026) following the acid guanidinium thiocyanate-phenol-chloroform extraction method (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). RNA quality assessment employed NanoDrop\u0026trade; One spectrophotometry and Qubit 3.0 fluorometry, and RNA integrity was further evaluated by agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003eStranded RNA-seq libraries were constructed from 2 \u0026micro;g total RNA using the KC\u0026trade; Stranded mRNA Library Prep Kit (Seqhealth, Wuhan, China). Sequencing was conducted on an Illumina NovaSeq 6000 platform (150 bp paired-end reads). Following adapter trimming and quality filtering, clean reads underwent alignment to the human reference genome using HISAT2 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Transcript quantification employed FPKM (fragments per kilobase of transcript per million mapped reads) normalization.\u003c/p\u003e \u003cp\u003eTranscriptomic and Splicing Analysis\u003c/p\u003e \u003cp\u003eDifferential gene expression analysis utilized edgeR (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), applying statistical thresholds of adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |fold change| \u0026ge; 2. Alternative splicing event (ASE) were identified and quantified using the ABLas pipeline, as previously described (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), which classifies ten splicing event types based on splice junction reads. Statistical significance was assessed via Fisher\u0026rsquo;s exact test. Regulated ASEs (RASEs) were defined by |ΔPSI| \u0026ge; 0.1 and adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; genes harboring RASEs were designated regulated alternative splicing genes (RASGs).\u003c/p\u003e \u003cp\u003eFunctional Enrichment Analysis\u003c/p\u003e \u003cp\u003eGene Ontology (GO) biological process and KEGG pathway enrichment analyses were conducted using KOBAS 2.0 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), with Benjamini-Hochberg false discovery rate (FDR) correction. Enriched terms satisfied FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eRT-qPCR Validation\u003c/h3\u003e\n\u003cp\u003eTotal RNA reverse transcription employed the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). Quantitative PCR was performed using SYBR Green Master Mix on a QuantStudio Real-Time PCR System. Gene expression levels were normalized to GAPDH and quantified using the 2^-ΔΔCt method (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Primer sequences are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAlternative splicing validation targeted specific isoforms using isoform-specific primer pairs designed to flank relevant splice junctions.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) from three independent biological replicates. Statistical comparisons employed Student\u0026rsquo;s t-test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses utilized R software (version 4.0.3) and GraphPad Prism 9.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eFMR1 Overexpression Reshapes Global Transcriptional Profiles\u003c/h2\u003e\n \u003cp\u003eRNA sequencing comparison between FMR1-overexpressing and control KGN cells identified 3,008 differentially expressed genes (DEGs) satisfying |fold change| \u0026ge; 2 and adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 criteria. Among these, 1,531 genes exhibited increased expression, while 1,477 displayed decreased expression in FMR1-overexpressing cells (Fig.\u0026nbsp;1D-E). Principal component analysis (PCA) based on FPKM values demonstrated clear separation between FMR1-overexpressing and control samples (Fig.\u0026nbsp;1C). Volcano plot visualization (Fig.\u0026nbsp;1D) illustrated extensive transcriptional changes associated with FMR1 overexpression.\u003c/p\u003e\n \u003cp\u003eRT-qPCR results comparing control and FMR1-overexpressing samples. Error bars represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. (B) Western blot analysis confirming successful FMR1 overexpression. (C) Principal component analysis (PCA) based on FPKM values of all detected genes in FMR1-overexpressing cells. The ellipse for each group represents the confidence ellipse. (D) Volcano plot displaying all differentially expressed genes (DEGs) between overexpression (OE) and control (Ctrl) samples. (E) Hierarchical clustering heatmap showing expression levels of all DEGs.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eFunctional Enrichment Reveals Immune Activation and Metabolic Suppression\u003c/h3\u003e\n\u003cp\u003eGO biological process enrichment analysis of upregulated genes revealed significant enrichment of pathways related to the type I interferon signaling pathway, defense response to virus, innate immune response, and inflammatory response (Fig.\u0026nbsp;2A, left panel). KEGG pathway analysis supported these observations, identifying enrichment in influenza A, measles, herpes simplex infection, and cytokine-cytokine receptor interaction pathways (Supplementary Figure S1).\u003c/p\u003e\n\u003cp\u003eConversely, downregulated genes were primarily enriched in cholesterol biosynthetic process, steroid metabolic process, lipid biosynthetic process, and cellular response to oxidative stress (Fig.\u0026nbsp;2A, right panel). KEGG analysis further highlighted enrichment in steroid biosynthesis, terpenoid backbone biosynthesis, and metabolic pathways (Supplementary Figure S1).\u003c/p\u003e\n\u003cp\u003eCollectively, these patterns indicate FMR1 overexpression is associated with enhanced immune-related activity and reduced steroid and lipid biosynthetic capacity in granulosa cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. KEGG pathway enrichment of FMR1-regulated DEGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar plot exhibiting the top 10 most enriched KEGG pathways for upregulated (left panel) and downregulated (right panel) DEGs.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eRT-qPCR Validates Expression Changes in Key Immune and Metabolic Genes\u003c/h2\u003e\n \u003cp\u003eWe selected seven representative genes spanning distinct functional categories for RT-qPCR validation. Among upregulated genes, BMP4, IRF7, IFI27, FAT1, and PLAU all demonstrated significantly increased expression in FMR1-overexpressing cells compared with controls (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;2B). Downregulated genes FABP3 and CSF1 exhibited significantly decreased expression (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;2C). These validation results were strongly consistent with RNA-seq data.\u003c/p\u003e\n \u003cp\u003eScatter plot exhibiting the most enriched GO biological process results of upregulated (left panel) and downregulated (right panel) DEGs. (B) Bar plot showing expression patterns and statistical differences for selected upregulated genes. Error bars represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. (C) Bar plot showing expression patterns and statistical differences for selected downregulated genes. Error bars represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eFMR1 Overexpression Extensively Remodels the Alternative Splicing Landscape\u003c/h2\u003e\n \u003cp\u003eBeyond transcriptional regulation, we next investigated the impact of FMR1 overexpression on alternative splicing. ABLas pipeline analysis identified 2,300 regulated alternative splicing genes (RASGs) and 5,234 regulated alternative splicing events (RASEs) in response to FMR1 overexpression, spanning ten event types (Fig.\u0026nbsp;3A).\u003c/p\u003e\n \u003cp\u003eThe most prevalent event type was exon skipping (SE), followed by alternative 5\u0026prime; splice site (A5SS) and alternative 3\u0026prime; splice site (A3SS) events. This distribution is consistent with established alternative splicing patterns observed in mammalian cells.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eIntersection Analysis Identifies Genes Subject to Dual-Layer Regulation\u003c/h2\u003e\n \u003cp\u003eTo identify genes subject to both transcriptional and post-transcriptional regulation by FMR1, we conducted an intersection analysis between DEGs and RASGs, identifying 208 genes (Fig.\u0026nbsp;3B). Go enrichment analysis of these dual-regulated genes highlighted innate immune response, defense response to virus, type I interferon signaling pathway, as well as lipid metabolic process and related metabolic pathways as the most significantly enriched biological processes (Fig.\u0026nbsp;3C). These findings indicate that FMR1 coordinates immune-related gene regulation and influences metabolic processes through both expression modulation and alternative splicing.\u003c/p\u003e\n \u003cp\u003eBar plot showing the statistical distribution of FMR1-regulated alternative splicing events (RASEs) by event type. (B) Venn diagram showing 208 overlapping genes between 2,300 RASGs (regulated alternative splicing genes) and 3,008 DEGs (differentially expressed genes), indicating dual-layer regulation by FMR1. (C) Scatter plot exhibiting the most enriched GO biological process results of the 208 overlapping genes between RASGs and DEGs, highlighting enrichment in innate immune response, response to virus, and immune system processes. (D) FMR1 regulates alternative splicing of CSF1. Left panel: IGV-sashimi plot showing regulated alternative splicing events and binding sites across mRNA. Reads distribution of RASE is plotted in the upper panel and transcripts are shown below. Right panel: Schematic diagrams depicting ASE structures with RT-qPCR validation shown at bottom. Error bars represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. (E) FMR1 regulates alternative splicing of IRF7. Layout and annotations as in panel D.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eRT-qPCR Validates Alternative Splicing Changes\u003c/h2\u003e\n \u003cp\u003eWe validated alternative splicing changes in four genes using isoform-specific RT-qPCR assays. IFI27 showed a significantly decreased splicing ratio in FMR1-overexpressing cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Figure S2B). Conversely, LAMC2, IRF7, and CSF1 all exhibited significantly increased splicing ratios (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;3D\u0026ndash;E and Supplementary Figure S2A). Of particular interest, LAMC2 displayed altered splicing without significant changes in overall gene expression, indicating that FMR1 can regulate transcript isoform composition independently of transcript abundance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSupplementary Figure S2. FMR1 regulates LAMC2 and IFI27 splicing.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFMR1 regulates alternative splicing of LAMC2. Left panel: IGV-sashimi plot showing regulated alternative splicing events and binding sites across mRNA. Reads distribution of RASE is plotted in the upper panel and transcripts are shown below. Right panel: Schematic diagrams depicting ASE structures with RT-qPCR validation shown at bottom. Error bars represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. (B) FMR1 regulates alternative splicing of IFI27. Layout and annotations as in panel A.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Findings and Biological Implications\u003c/h2\u003e \u003cp\u003eThis investigation identifies FMR1 as a central regulatory node coordinating transcriptional and post-transcriptional programs in human granulosa cells. Three principal findings emerge: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) FMR1 overexpression is associated with activation of immune-related pathways accompanied by suppression of steroidogenic processes, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) FMR1 exerts broad effects on the alternative splicing landscape, particularly affecting RNA processing machinery, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) 208 genes undergo coordinated dual-layered regulation, experiencing coordinated changes in both expression levels and isoform composition.\u003c/p\u003e \u003cp\u003eTo our knowledge, this research is the first to systematically elucidate that FMR1 coordinates immune-related transcriptional programs and alternative splicing regulation in human granulosa cells, establishing a dual-layer regulatory framework relevant to FXPOI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFMR1\u0026rsquo;s Role in Granulosa Cell Immune Function\u003c/h2\u003e \u003cp\u003eThe pronounced enrichment of upregulated genes in type I interferon signaling and antiviral defense pathways suggests FMR1 may shift granulosa cells toward a heightened immune-surveillance-like state. This observation aligns with growing evidence implicating inflammatory signaling in ovarian aging and follicular attrition(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKey upregulated immune mediators warrant specific discussion. IRF7, encoding interferon regulatory factor 7, serves as a master transcriptional activator of type I interferon responses (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Persistent IRF7 activation has been linked to autoimmune disease pathogenesis (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Additionally, IRF7 itself undergoes extensive alternative splicing, generating isoforms with distinct functional properties in viral defense (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Our finding that IRF7 exhibits both elevated expression and altered splicing suggests a multilayered mode of immune regulation by FMR1.\u003c/p\u003e \u003cp\u003eIFI27 (interferon alpha-inducible protein 27) represents another interferon-stimulated gene showing dual regulation. Recent investigations demonstrate IFI27\u0026rsquo;s complex immunomodulatory functions, including both positive and negative regulation of antiviral signaling depending on cellular context(\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The combination of increased IFI27 expression but decreased splicing ratio in our data raises the possibility of isoform-specific functional divergence, rather than uniform amplification of IFI27 activity.\u003c/p\u003e \u003cp\u003eThe autoimmune component of POI pathogenesis is well-established (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Chronic immune activation in granulosa cells could generate inflammatory microenvironments detrimental to follicular development and survival. Whether FMR1-driven immune activation represents an adaptive response to cellular stress or maladaptive process contributing to follicular depletion remains an important question for future investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFMR1\u0026rsquo;s Impact on Metabolic Homeostasis\u003c/h2\u003e \u003cp\u003eDownregulation of cholesterol biosynthesis and steroid metabolism genes presents another critical finding. FABP3 (fatty acid binding protein 3), regulator of fatty acid transport and PPARα signaling, plays essential roles in lipid trafficking and energy metabolism (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). CSF1 (colony stimulating factor 1), while primarily known for immune cell regulation, has also been implicated in systemic metabolic regulation, including the mobilization of adipose fat and modulation of hepatic glucose uptake through effects on macrophage populations (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSteroid hormone synthesis depends critically on cholesterol as substrate. Impaired steroidogenic capacity could compromise granulosa cell endocrine function, contributing to follicular dysfunction. The inverse relationship between immune activation and metabolic function suggests that FMR1 dysregulation may force granulosa cells into functional trade-offs between immune surveillance and reproductive competence, a phenomenon observed across evolutionary biology. These findings provide a mechanistic explanation for the steroidogenic suppression highlighted in the Abstract and support a functional link between FMR1 dysregulation and impaired granulosa cell endocrine capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAlternative Splicing as a Regulatory Layer\u003c/h2\u003e \u003cp\u003eThe identification of 4,077 genes undergoing FMR1-regulated alternative splicing highlights the magnitude of post-transcriptional control exerted by this gene. Enrichment of RNA splicing, translation, and protein folding processes suggests that FMR1 influences not only individual target genes but also the core gene expression machinery itself, potentially establishing regulatory feedback circuits.\u003c/p\u003e \u003cp\u003eFMRP\u0026rsquo;s nuclear functions in splicing regulation are increasingly recognized(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Our data demonstrate that his regulatroy role extends beyond neuronal tissue to granulosa cells, with potential relevance to FXPOI pathogenesis. The RNA toxicity hypothesis suggests CGG-repeat expansions sequester splicing factors (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e); our findings provide functional support for this model by revealing widespread splicing perturbations associated with FMR1 dysregulation.\u003c/p\u003e \u003cp\u003eThe subset of 208 dual-regulated genes represents a particularly important regulatory layer. These genes experience coordinated control at both transcriptional and post-transcriptional levels, suggesting they may constitute core functional targets of FMR1-mediated regulation. Their enrichment in immune processes reinforces the central role of immune dysregulation in FMR1-associated ovarian dysfunction.\u003c/p\u003e \u003cp\u003eOur studies expand the functional scope of FMR1 beyond translational control, positioning alternative splicing dysregulation as a central and previously underappreciated mechanism contributing to ovarian dysfunction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLAMC2 as an Example of Pure Splicing Regulation\u003c/h2\u003e \u003cp\u003eLAMC2 (laminin subunit gamma 2) provides an instructive example of post-transcriptional regulation independent of transcript abundance. This gene exhibited significantly altered splicing ratios without accompanying expression changes, exemplifying alternative splicing as an independent regulatory mechanism. Laminins constitute essential extracellular matrix components influencing cell adhesion, migration, and signaling (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Distinct LAMC2 isoforms likely possess differential functional properties relevant to granulosa cell-extracellular matrix interactions critical for folliculogenesis.\u003c/p\u003e \u003cp\u003eThis observation underscores that analyses focused exclusively on expression levels would overlook substantial regulatory complexity. Alternative splicing expands proteomic diversity and functional adaptability beyond what transcript abundance alone can capture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Translational Implications\u003c/h2\u003e \u003cp\u003eThese molecular insights carry potential translational significance for FXPOI management. First, immune-related genes exhibiting dual regulation (IRF7, IFI27, CSF1) represent candidate biomarkers for POI risk stratification among premutation carriers. Second, the immune activation signature observed immunomodulatory interventions may merit exploration as fertility-preserving interventions, although careful consideration of immune system complexity and context-dependence is essential.\u003c/p\u003e \u003cp\u003eThird, identification of specific splice isoforms altered by FMR1 dysregulation raises the possibility of splice‑switching oligonucleotide‑based therapies, an approach that has shown therapeutic promise by correcting aberrant splicing and restoring functional protein expression in genetic disorders such as fragile X syndrome (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Targeting pathological splice variants while preserving physiological isoforms could enable precision therapeutic strategies.\u003c/p\u003e \u003cp\u003eFourth, metabolic support approaches aimed at restoring steroidogenic function could supplement immune-targeted approaches. However, clinical trials evaluating metabolic supplements, such as coenzyme Q10, in diminished ovarian reserve have yielded mixed results(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), suggesting that metabolic correction alone may be insufficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMechanistic Insights Into FXPOI Pathogenesis\u003c/h2\u003e \u003cp\u003eOur findings provide mechanistic support for the RNA toxicity hypothesis while extending its scope. If CGG-repeat expansions sequester splicing regulators, the resulting splicing dysregulation could manifest as widespread isoform imbalances, particularly affecting immune and metabolic pathways critical for ovarian homeostasis. The combination of immune hyperactivation and metabolic dysfunction could create conditions incompatible with sustained folliculogenesis.\u003c/p\u003e \u003cp\u003eAn alternative interpretation is that some observed changes represent compensatory rather than primary pathogenic responses. Immune activation may reflect granulosa cells\u0026rsquo; attempts to mitigate cellular stress imposed by FMR1 dysregulation. Distinguishing adaptive from maladaptive responses will require time-resolved and functional studies, which cannot be addressed by the current cross-sectional design.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eComparison With Existing Literature\u003c/h2\u003e \u003cp\u003eOur findings align with and extend previous work demonstrating FMR1\u0026rsquo;s roles in follicle activation via PI3K/AKT/mTOR signaling and autophagy regulation(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). We add an additional regulatory dimension\u0026mdash;immune signaling and alternative splicing\u0026mdash;to FMR1\u0026rsquo;s ovarian functions.\u003c/p\u003e \u003cp\u003eRecent studies documented extensive FMRP-regulated splicing in neurons (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), whereas ovarian splicing regulation by FMR1 has remained largely unexplored. Our data demonstrate this regulatory function extends to granulosa cells, with functional consequences potentially relevant to FXPOI.\u003c/p\u003e \u003cp\u003eSingle-cell transcriptomic analyses of POI patient ovaries revealed altered immune cell populations and granulosa cell-immune cell interactions (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Our findings provide molecular mechanisms that may underlie these cellular-level observations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research Directions\u003c/h2\u003e \u003cp\u003eSeveral research avenues emerge from these findings:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eModel system validation\u003c/b\u003e: Findings should be validated in primary human granulosa cells and patient-derived iPSCs. Engineering CGG repeat expansions into model systems would better recapitulate FXPOI molecular pathology than overexpression alone.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLongitudinal studies\u003c/b\u003e: Prospective molecular profiling of young premutation carriers prior to POI onset may enable early biomarkers discovery.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMechanistic investigations\u003c/b\u003e: ChIP-seq, RIP-seq, and loss-of-function experiments are needed to establish causal relationships and determine the functional consequences of specific splice isoforms.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntervention studies\u003c/b\u003e: Testing immune-modulatory or splicing-corrective strategies in animal models could provide preclinical evidence for therapeutic development.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eStudy Limitations\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. First, KGN cells, while widely used, originate from a granulosa cell tumor and may not fully recapitulate normal physiology. Second, overexpression models differ from premutation biology involving CGG expansions. Third, transcriptomic analyses provide correlative rather than causal insights. Fourth, only a single post-transduction timepoint was examined. Finally, in vivo validation remains necessary to confirm relevance in whole-organism contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis investigation identifies FMR1 as a central coordinator of dual-layered programs in human granulosa cells. Through simultaneous modulation of transcript abundance and alternative splicing, FMR1 profoundly influences immune signaling, metabolic homeostasis, and cellular stress responses\u0026mdash;all critical determinants of ovarian function. Together, these findings extend current models of FXPOI by integrating immune dysregulation and alternative splicing defects as convergent downstream consequences of FMR1 dysfunction.\u003c/p\u003e \u003cp\u003eThe immune dysregulation and metabolic impairment observed offer mechanistic insight to FXPOI pathogenesis, while widespread splicing alterations reveal previously unrecognized regulatory complexity. The 208 genes to coordinated transcriptional and post-transcriptional control represent a core FMR1-controlled network, enriched in immune pathways, and likely critical for ovarian homeostasis.\u003c/p\u003e \u003cp\u003eThe identification of specific genes (IRF7, IFI27, FAT1, LAMC2, CSF1) exhibiting coordinated regulation provides concrete targets for biomarker development and therapeutic intervention. Translating these cellular insights into clinical strategies may ultimately improve fertility preservation and quality of life for women carrying FMR1 premutation alleles.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASEs, alternative splicing events; DEG, differentially expressed gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NC, negative control; OE, overexpression; POI, premature ovarian insufficiency; FXPOI, fragile X\u0026ndash;associated primary ovarian insufficiency; RASEs, regulated alternative splicing events; RASGs, regulated alternative splicing genes; RBPs, RNA-binding proteins; FMRP, fragile X mental retardation protein\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eAll experiments were performed in accordance with institutional guidelines. The KGN cell line was obtained from commercial sources with appropriate documentation.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Lan Chao, Shuo Zhao.Methodology: Shuo Zhao, Dong Hou, Chengzi Huang.Formal analysis: Shuo Zhao, Shan Dong, Yikai Chen.Investigation: Shuo Zhao, Dong Hou, Chengzi Huang, Shasha Qi.Data curation: Shan Dong, Lili Wang.Visualization: Shuo Zhao, Yikai Chen.Writing \u0026ndash; original draft: Shuo Zhao.Writing \u0026ndash; review \u0026amp; editing: Lan Chao, Lili Wang.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the staff of the sequencing platform for their technical support in RNA sequencing and data processing. We also acknowledge all members of the Center for Reproductive Medicine, Qilu Hospital of Shandong University, for helpful discussions and technical assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics \u0026amp; Bioinformatics 2025) in National Genomics Data Center (Nucleic Acids Res 2025), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA016706) that are publicly accessible at\u0026nbsp;https://ngdc.cncb.ac.cn/gsa-human.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEuropean Society for Human R, Embryology Guideline Group on POI, Webber L, Davies M, Anderson R, Bartlett J, et al. ESHRE Guideline: management of women with premature ovarian insufficiency. 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Genomics Proteomics Bioinformatics; 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"FMR1, premature ovarian insufficiency, alternative splicing, post-transcriptional regulation, immune-related pathways, granulosa cells, RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-8654777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8654777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe fragile X mental retardation 1 (FMR1) gene is strongly implicated in ovarian function, and premutation expansions of FMR1 represent the most common monogenic cause of premature ovarian insufficiency (POI). However, the ovarian-specific molecular mechanisms by which FMR1 dysregulation contributes to ovarian dysfunction remain poorly understood. In particular, the roles of FMR1 in coordinating gene expression and alternative splicing in granulosa cells have not been systematically explored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing lentiviral-mediated overexpression, we established stable FMR1-expressing KGN cell lines alongside wild-type controls, subsequently interrogating their transcriptomes through bulk RNA sequencing. Differential expressed genes were assessed using edgeR, followed by functional annotation using KOBAS 2.0 for Gene Ontology (GO) biological processes and KEGG pathway enrichment analyses. Alternative splicing events (ASEs) were identified with the ABLas pipeline. Selected genes and splicing events were validated by RT-qPCR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFMR1\u003c/em\u003e overexpression induced substantial transcriptional reprogramming, with 3,008 differentially expressed genes identified\u0026mdash;1,531 showing increased abundance, 1,477 decreased. Upregulated genes were predominantly enriched in type I interferon signaling and antiviral immune response pathways, whereas downregulated genes were mainly associated with cholesterol biosynthesis and steroid metabolic processes. RT-qPCR confirmed increased expression of \u003cb\u003eBMP4, IRF7, IFI27, FAT1\u003c/b\u003e, and \u003cb\u003ePLAU\u003c/b\u003e, together with reduced expression of \u003cb\u003eFABP3\u003c/b\u003e and \u003cb\u003eCSF1\u003c/b\u003e (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In parallel, \u003cem\u003eFMR1\u003c/em\u003e overexpression led to widespread alterations in alternative splicing across 4,077 genes, enriched for translation machinery, RNA processing, and splicing regulation. Intersection analysis identified 208 genes exhibiting coordinated changes in both expression levels and splicing patterns, with significant enrichment in innate immunity and cellular metabolism. RT-qPCR further confirmed significant splicing in four targets: \u003cb\u003eIFI27\u003c/b\u003e demonstrated reduced splice-form ratios (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while \u003cb\u003eLAMC2\u003c/b\u003e, \u003cb\u003eIRF7\u003c/b\u003e, and \u003cb\u003eCSF1\u003c/b\u003e exhibited elevated ratios (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings reveal that \u003cem\u003eFMR1\u003c/em\u003e regulates immune-related and metabolic gene networks in human granulosa cells through coordinated modulation of gene expression and alternative splicing. This dual-layer regulatory effect provides mechanistic insight into how \u003cem\u003eFMR1\u003c/em\u003e dysregulation may contribute to ovarian dysfunction and FXPOI pathogenesis, and highlights alternative splicing as an important but previously underappreciated regulatory mechanism in ovarian biology.\u003c/p\u003e","manuscriptTitle":"FMR1 regulates immune-related gene expression and alternative splicing in human granulosa cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 12:09:15","doi":"10.21203/rs.3.rs-8654777/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":"8d6de1b3-ff99-4704-b38c-32506034618a","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T05:25:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 12:09:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8654777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8654777","identity":"rs-8654777","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}