Masuma Khatun

Background: Induced pluripotent stem cells (iPSCs) from patients have been little used to model polyendocrine metabolic ovarian syndrome (PMOS, formerly polycystic ovary syndrome, PCOS), and differences between unrelated donors can masquerade as disease effects. Earlier comparisons of three or four donors per group reported hundreds to thousands of differentially expressed genes. We asked whether a disease signature is detectable in a larger, genotyped panel, balanced for BMI category and BMI-adjusted, of full-phenotype patients. Methods: We studied 14 clonal, transgene-free iPSC lines reprogrammed by CRISPR activation from endometrial stromal fibroblasts of seven women with PMOS meeting all three Rotterdam criteria, and seven controls frequency-matched for BMI category (30 kg/m²). Bulk RNA-seq (16,489 expressed genes) was related to diagnosis, hormones, metabolic traits, age, and X-inactive specific transcript (XIST) status using gene-wise models, exact enumeration of all 3,432 group splits, and permutation-calibrated gene-set tests. Donors were genotyped within a cohort of 140 women and scored with an imputation-based genome-wide PMOS polygenic risk score (PRS). PMOS risk-locus genes were examined against a 202-line iPSC proteome. Results: PMOS donors had higher anti-Müllerian hormone (AMH), testosterone and free androgen index than controls. No gene was differentially expressed between PMOS and control lines (false discovery rate < 0.1), and the number of nominally associated genes was no greater than for random 7-versus-7 splits of the lines (p = 0.75). This was unchanged with an alternative read quantification and with count-based models. With seven donors per group, the design could detect 1.8-fold differences per gene, compared with roughly 3-to 6-fold for the three-or four-donor designs used previously. Neither diagnosis nor any hormonal or metabolic trait explained more expression variance than chance, and neither PMOS GWAS-locus genes nor Hallmark pathways were enriched. Two control lines showed near-complete loss of XIST, potentially indicative of X-chromosome inactivation (XCI) erosion, and a third was borderline. Endocrine PMOS-locus genes (e.g., FSHR, AMH) were not expressed, whereas DNA-repair and growth genes were. Across 129 genotyped women, the PRS was higher in PMOS (odds ratio 1.81 per SD; AUC 0.65), but PMOS and control iPSC donors overlapped fully in polygenic risk. Conclusions: In this panel of 14 undifferentiated iPSC lines from women with the full PMOS phenotype, no robust PMOS-associated transcriptomic differences were detected. Expression variation was dominated by line-to-line differences, consistent with donor background as their main source, although with one clone per donor a contribution of clonal variation cannot be excluded. Reduced XIST expression in some control lines is potentially indicative of, but does not establish, X-chromosome inactivation erosion. Disease-associated differences in differentiated, hormone-responsive cells cannot be excluded, and differences reported in smaller panels may partly reflect inter-donor variability. Future studies should use adequately sized panels, control for XIST erosion, select donors by polygenic risk, and examine hormone-responsive derivatives.

Gynecological conditions including endometriosis, polyendocrine metabolic ovarian syndrome and malignancies cause widespread chronic morbidity and infertility, yet translating genetic discoveries into biological mechanisms remains challenging. We propose a framework integrating biobank-derived human genetic data, patient-derived stem cell models, and advanced tissue engineering. By combining naturally occurring genetic variation with targeted genome engineering and high-resolution molecular analyses, this approach promises to enable mechanistic insights and therapeutic discovery. We discuss methodological, ethical, and translational challenges, including polygenic risk score construction, variant prioritization, ancestry-related limitations, and modeling hormonal microenvironments. Together, this framework offers a roadmap for advancing functional genomics and precision medicine across gynecological diseases.

Background: Systemic lupus erythematosus (SLE) has a complex polygenic architecture, but translating genome-wide association signals into biologically interpretable candidates remains challenging. We applied an integrative post-GWAS framework to refine SLE-associated loci and prioritize candidate regulatory mechanisms.Methods: European-ancestry SLE GWAS summary statistics from FinnGen and Bentham et al. were meta-analysed, comprising 8,417 cases and 354,277 controls. After quality filtering, 6,782,131 SNPs were retained. Downstream analyses included LAVA regional prioritization, SuSiE fine-mapping, Bayesian colocalization with GTEx v10 eQTLs, independent replication in the Julià et al. Spanish cohort, pathway enrichment, cross-trait genetic correlation, and therapeutic annotation.Results: The discovery meta-analysis identified 47 genome-wide significant SLE-associated loci. LAVA identified 14 high-confidence regions, of which nine were retained as the primary prioritized set based on LAVA support and/or convergent regulatory evidence. SuSiE fine-mapping generated 26 credible sets across the prioritized loci. The strongest association mapped to the MHC region near CLIC1 (rs389884), with colocalization support in spleen and whole blood. Because the CLIC1-region lead variant was unavailable for replication, this signal was interpreted as an emerging candidate for functional validation rather than a replicated causal signal. Seven available variants replicated with concordant effects. Pathway analyses highlighted type I interferon, JAK–STAT signaling, cytokine regulation, and antigen presentation, while cross-trait analyses showed the strongest correlations with Sjögren syndrome and systemic sclerosis.Conclusions: This integrative post-GWAS analysis refines SLE association signals into biologically interpretable candidate regions and supports interferon and JAK–STAT signaling as central genetically supported pathways in SLE.

Polyendocrine metabolic ovarian syndrome (PMOS) is a complex endocrine–metabolic disorder affecting up to 18% of women worldwide and remains the leading cause of anovulatory infertility. Despite extensive research, current treatments primarily target symptoms, including menstrual irregularities, hyperandrogenism, and metabolic dysfunction, without addressing the underlying molecular and tissue-level disturbances. Advances in multi‑omic profiling have identified disruptions across neuroendocrine, metabolic, inflammatory, and extracellular matrix pathways, alongside genetic susceptibility at loci such as DENND1A , CYP17A1 , LHCGR , FSHR , IRS1 , and PPARG . However, the functional roles of many variants remain unresolved. CRISPR/Cas9 gene editing enables precise interrogation of these pathways, while stem cell–based platforms, including mesenchymal stem cells (MSCs), exosomes, and gene-edited induced pluripotent stem cells (iPSCs), may serve as complementary platforms for regeneration and disease modeling. Preclinical studies demonstrate that MSCs and their derivatives modulate inflammation, restore ovarian structure, and improve metabolic parameters, while iPSC-based models enable patient-specific investigation of steroidogenic and metabolic abnormalities. Translational challenges remain, including targeted delivery, off-target effects, phenotypic heterogeneity, and regulatory considerations. Integrating CRISPR‑based functional genomics with stem cell research may shift PMOS management from symptom‑focused care to targeted, mechanism‑driven interventions that could modify the course of PMOS (Graphical Abstract). Graphical Abstract Mapping the paradigm shift from symptomatic management to mechanism-driven precision medicine in Polyendocrine Metabolic Ovarian Syndrome (PMOS). The left panel summarizes current clinical challenges defined by purely symptomatic treatments for anovulatory infertility, hyperandrogenism, and metabolic dysfunction, a limitation sustained by a historically poor understanding of underlying disease mechanisms. The middle panel outlines novel biotechnological platforms for mechanistic discovery and therapeutic intervention. Gene function interrogation uses CRISPR/Cas9 editing to target specific risk loci, including DENND1A , CYP17A1 , IRS1 , and PPARG , to dissect functional pathways governing androgen excess, follicular arrest, and insulin resistance. Stem cell-based strategies employ mesenchymal stem cells (MSCs) and MSC-derived exosomes to modulate systemic inflammation, restore ovarian physiological function, and improve homeostatic metabolic parameters. Patient-specific disease modeling utilizes induced pluripotent stem cells (iPSCs) differentiated into granulosa-like cells or in vitro endometrial models to recapitulate distinct steroidogenic and metabolic abnormalities. The right panel illustrates the ultimate clinical goal of these integrated approaches, demonstrating how transitioning to targeted therapies establishes a balanced endocrine-metabolic profile, improves reproductive function, and reduces long-term metabolic risks, successfully moving the field toward mechanism-driven care and precision medicine. This figure was created with BioRender.com.

Polyendocrine metabolic ovarian syndrome (PMOS) is a prevalent endocrine disorder with complex reproductive, metabolic, and psychological features. A total of fourteen induced pluripotent stem cell (iPSC) lines were generated from endometrial stromal fibroblasts using a CRISPRa-based reprogramming method, comprising seven iPSC lines obtained from women with PMOS (all three Rotterdam criteria) and seven from body mass index (BMI)-matched non-PMOS controls. This well-characterized resource provides a unique platform for investigating cell-intrinsic PMOS mechanisms, supporting targeted pathogenesis studies and translational advances.

Infertility generates profound psychological and social distress for both women and men, yet men’s communicative experiences remain comparatively underexamined. Male infertility (MI) is often shaped by stigma, norms of masculinity, and limited opportunities for emotional disclosure, constraining help-seeking in offline settings. This study investigates how men use anonymous online peer-support spaces to discuss MI by analyzing discussions from the r/maleinfertility subreddit on Reddit. Using natural language processing techniques, we examined 10,769 posts and 80,381 comments published between 2013 and 2025. Analyses assessed sentiment and emotional expression, topic structure, hyperlink networks, and discussions related to diagnostic testing, treatment decision-making, and donor sperm use. Topic modeling revealed a functional differentiation between posts and comments. Original posts primarily focused on clinical sense-making, including interpretation of semen analyses, hormonal testing, and assisted reproduction options. In contrast, comments emphasized emotional validation, experiential knowledge-sharing, and normalization of alternative family-building pathways. Emotional expression varied by discussion topic, with heightened fear and sadness in conversations involving genetic testing, surgical sperm retrieval, and donor sperm. Hyperlink analysis indicated frequent engagement with peer-reviewed medical information, reflecting active evidence-seeking alongside peer exchange. Taken together, findings suggest that anonymous online communities function as critical infrastructures of support for men experiencing infertility, enabling forms of disclosure and vulnerability often constrained in offline contexts. These spaces facilitate interpretation of medical information, collective coping, and decision-making regarding treatment and donor options. The study highlights the role of digital anonymity in mitigating stigma and expanding communicative possibilities for men navigating infertility alongside clinical care.

Relapse risk in endometrial carcinoma (EC) is strongly influenced by molecular subtype, yet current WHO/ESGO classifications rely on postoperative data, limiting their utility for preoperative decision-making. We developed and compared interpretable machine learning (ML) models to predict relapse timing (none, ≤6 months, >6 months) using exclusively preoperative multimodal data. In a retrospective cohort of 784 EC patients, we integrated clinicopathological, molecular, immunohistochemical, and systemic biomarkers and constructed four feature strategies: (1) Traditional (clinicopathology), (2) ESGO (guideline risk groups), (3) TP53 + MMRd (high-risk biology), and (4) POLE (low-risk biology). Classifiers (Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Gradient Boosting (GBM)) were trained with leakage-safe preprocessing and in-fold resampling; performance was evaluated via area under the curve (AUC), accuracy, recall, and F1 score, and interpretability via SHapley Additive exPlanations (SHAP). The RF-based Traditional model achieved the highest overall performance (F1 = 0.895, AUC = 0.84), while the GBM-based POLE model showed superior sensitivity (F1 = 0.886, AUC = 0.842). SHAP identified ARID1A loss, elevated CA125, thrombocytosis, and p16 expression among key predictors of relapse; while overlapping high-risk features across models included advanced stage, deeper myometrial invasion, elevated CA125, and positive cytology. These biologically coherent, explainable predictions support individualized risk stratification and may enhance preoperative decision-making, particularly for aggressive histology and high-risk molecular subtypes.

Bacterial extracellular vesicles (bEVs) produced by intestinal commensal bacteria mediate host-microbe interactions, but their proteomes have been explored in only a small number of species. We characterized bEVs from in vitro cultures of 12 species of Actinomycetota, Bacillota (Firmicutes), Bacteroidota, Fusobacteria, Pseudomonadota, and Verrucomicrobia. This is the first report of bEVs from Veillonella magna , an exceptional gram-negative Bacillota, and the gram-positives Peptostreptococcus russellii and Turicibacter sanguinis . The morphology and protein subcellular localization patterns of bEVs reflected the envelope structures of their parent bacteria. Notably, V. magna may be able to produce both outer membrane and cytoplasmic membrane vesicles. The proteome compositions were dictated by phylogeny, suggesting largely non-selective packaging of proteins. Annotation of protein functions indicated roles in nutrient metabolism and transport, and in host immune system modulation. Peptidoglycan modifying enzymes and the abundant bacteriophage proteins in Enterobacter cloacae and Limosilactobacillus reuteri bEVs may be involved in vesicle biogenesis. The functions of many abundant bEV proteins are unknown. The most abundant bEV proteins were largely species-specific, but we identified several conserved proteins that may be used as markers to distinguish commensal bEVs from host EVs. Comparison to previous in vitro and fecal metaproteomics data indicates that bEV proteome compositions are reproducible.