The impact of heterozygous BRCA1 mutations on ovarian angiogenesis

The impact of heterozygous BRCA1 mutations on ovarian angiogenesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The impact of heterozygous BRCA1 mutations on ovarian angiogenesis Roberta Bulla, Silvia Pegoraro, Barbara Fogar, Mariagiulia Spazzapan, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7980820/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BRCA1/2 mutations are classically associated with hereditary breast and ovarian cancer, yet growing evidence indicates that even heterozygous BRCA1 status may alter ovarian physiology before malignant transformation. Carriers of BRCA1 mutations display reduced ovarian reserve and accelerated reproductive ageing, but the cellular and molecular mechanisms behind these changes remain unclear. Given the fundamental role of angiogenesis in follicle survival and stromal homeostasis, we investigated whether BRCA1 haploinsufficiency disrupts the ovarian microvascular environment, predisposing to both impaired ovarian function and a pro-tumorigenic niche. We isolated ovarian endothelial cells (OVECs) from biopsies of healthy women, carrying and non-carrying the BRCA1 mutation. Our observations indicated distinct growth behaviours in vitro , particularly in terms of morphology and replication rate. Transcriptomic analysis revealed a distinct gene expression profile in mut compared to WT OVECs. Mut cells exhibited an enrichment of gene signatures associated with vascular remodelling, such as migration, proliferation, and sensitivity to endothelial-to-mesenchymal transition. Functionally, mut OVECs showed increased angiogenic behaviour and a shift toward mesenchymal traits. Histological analysis of ovarian tissues confirmed aberrant vascular architecture and increased microvessel density in BRCA1-mut ovaries, consistent with endothelial activation and remodelling. In conclusion, phenotypic and functional differences between wild-type and mut OVECs were proved, demonstrating that heterozygous mutations in BRCA1 can induce a tissue-specific endothelial dysfunction. Health sciences/Pathogenesis/Immunopathogenesis Biological sciences/Cancer/Tumour angiogenesis Angiogenesis endothelial cells BRCA1 mutations EndMT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction A considerable body of research has explored the role of BRCA1 depletion in cancer development, but limited information is available regarding the implications of the functional alterations associated with heterozygous BRCA1 status in ovarian tissue. Growing evidence has indicated that the BRCA1 mutation negatively affects the ovarian reserve of carriers and accelerates ovarian ageing, impacting both the quantity and quality of reproductive outcomes ( 1 ). It is important to emphasize that the survival and maturation process of follicles is directly related to ovarian angiogenesis ( 2 ). BRCA1 functions as a 'caretaker' of genome stability, influencing several crucial cellular processes ( e.g. , DNA damage repair, transcriptional regulation, ubiquitination, and cell-cycle control) ( 3 ). BRCA1 participates in various signalling pathways, including Homologous Recombination, P13K/AKT, apoptosis, and interaction with microRNAs, that can either upregulate or inhibit its function ( 4 ). Decreased expression of BRCA1 activates PI3K/AKT pathway, which further facilitates cell cycle progression ( 4 ). The PI3K/AKT pathway promotes angiogenesis by activating key endothelial cell (EC) functions ( e.g. , migration and proliferation), in both normal blood vessel development and tumour growth. Activation occurs through signals from growth factors, including VEGF, and contributes to the production of factors ( e.g. , nitric oxide and angiopoietins) supporting new blood vessel formation ( 5 , 6 ). Although several studies have clarified the pathogenetic mechanisms behind BRCA mutations, there is still a lack of analysis regarding the heterozygous BRCA1-mutated ovarian microenvironment. Given the high penetrance of BRCA1/2 mutations for breast and ovarian cancer risk (approximately 50%), research has focused on determining whether these germline mutations cause any genomic/epigenomic alterations before the loss of heterozygosity, particularly in epithelium of disease-relevant tissue types [ i.e. , the Fallopian tubes (FT) and breast] ( 7 ). The interaction between BRCA1-deficient tumour cells and the surrounding stroma promotes processes like EC survival and angiogenesis. As the tumour advances, angiogenesis becomes crucial, often leading to a transformation in which ECs undergo the endothelial-to-mesenchymal transition (EndMT) ( 8 ). EndMT is characterized by a loss of endothelial markers and the acquisition of mesenchymal/fibroblastic phenotypes. Research targeting these molecules has made anti-angiogenic and anti-EndMT therapies a promising approach in tumours ( 9 , 10 ), including ovarian cancer ( 11 ). To date, no direct evidence exists linking heterozygous mutations of BRCA1 to altered behaviour of ECs in the pre-neoplastic microenvironment. This study aimed to explore the impact of BRCA1 mutations on ovarian stroma, particularly focusing on ECs and their potential contribution to tumorigenesis. We investigated the differences in angiogenic behaviour, susceptibility to EndMT, phenotypic plasticity, and gene expression profiles between ovarian ECs (OVECs) isolated from patients with (mut) and without BRCA1 (WT) mutations. Moreover, we explored whether vascularization in mutated ovaries is different. Our novel findings may aid in creating effective early detection strategies for prevention of BRCA1 ovarian neoplastic transformation. 2 Materials and Methods 2.1. Patient enrolment Detailed information is provided in the Supplementary Information . 2.2. Cell isolation and culture OVECs were isolated and maintained as described by Agostinis et al. ( 12 ). Human Umbilical Vein Endothelial Cells (HUVECs) were isolated and maintained according to Jaffe et al. ( 13 ). Human Adult Dermal Microvascular Endothelial Cells (ADMECs) were obtained from skin biopsies of patients undergoing reductive plastic surgery, as described by Agostinis et al. ( 14 ). 2.3. Bioinformatics analyses 2.3.1 RNA-seq data preprocessing Raw paired-end FASTQ files were quality-checked using FastQC (v0.11.9). Adapter sequences and low-quality bases were trimmed as required. Transcript-level quantification was performed with Salmon (v1.9.0) in quasi-mapping mode using the Ensembl GRCh38 reference transcriptome (release 109; Homo_sapiens.GRCh38_decoy.index). Salmon was run with sequence bias and GC bias correction enabled. Quantification outputs were imported into R (v4.3.2) using the tximport package, and transcript counts were summarized at the gene level using the Ensembl v109 annotation (GTF). 2.3.2 Normalization and differential expression analysis Raw count matrices were filtered to remove lowly expressed features. Counts were normalized by size factors estimated with the DESeq2 package (v1.40). Differential expression analyses compared BRCA1-mutated ( n = 6) vs WT ( n = 6) OVECs. Differentially expressed transcripts (DETs) were defined by an adjusted p-value 1. Principal Component Analysis (PCA) was performed on variance-stabilized counts using DESeq2 . Heatmaps were generated with the ComplexHeatmap package, and Z-scores were computed by row-standardization of normalized counts. 2.3.3 Functional and regulator analyses Functional annotation of DETs was performed with Ingenuity Pathway Analysis (IPA, Qiagen). Categories of Diseases and Functions and Canonical Pathways were ranked by –log₁₀ p-value and visualized with barplots with the ggpubr package. Upstream regulator analysis and regulator effect prediction were carried out in IPA, and predicted activation states were represented as Z-scores (blue = inhibition, red = activation). Networks were exported from IPA to visualize relationships between upstream transcription factors, signalling molecules, and DETs via Cytoscape software. 2.3.4 Signature benchmarking DET list was compared against a repository of 478 literature-derived gene sets using Rummagene. Signature enrichment was evaluated using the singscore method and visualization performed with ggpubr package. Significant enrichments were defined as FDR-adjusted p < 0.05. EndMT-related signatures were specifically examined, including those from ( 15 – 17 ). 2.3.5 Software and reproducibility All analyses were performed in R (v4.3.2) using Bioconductor packages, with code executed under Linux. Custom R scripts for QC, normalization, DET detection, and visualization are available upon request. 2.4. Angiogenesis assays Migration and wound healing assays were performed as described in ( 12 , 18 ). Cells were stimulated with 20% human serum (HS) as positive control. Tube formation assay was performed as described in ( 12 ). Proliferation assay was performed seeding 7x10 3 ECs/well in a 96-well plate, then stimulating them with TGFβ (10 ng/mL) or 20% HS as positive control. After 24h, MTS was added to each well, and cell proliferation was measured using Spark Plate Reader (Tecan) (450 nm). 2.5. Antibodies Antibodies used in immunohistochemistry (IHC), immunofluorescence (IF), and flow cytometry analyses, are listed in Supplementary Table S1 . 2.6. Histo- and Immunohistochemical analysis for vessel identification Immunohistochemical analysis was performed as described in ( 19 ). 2.7 QuPath Vessel quantification Detailed information is provided in Supplementary Information . 2.8 Flow cytometry Flow cytometry analysis was performed as described in ( 12 ). 2.9. Immunofluorescence Immunofluorescence staining was performed as described in ( 14 ). 2.10 Morphological analysis Morphological analysis was performed using ImageJ software. Morphometric values of circularity, roundness (or aspect ratio, AR), Feret diameter, and perimeter of the cells were obtained from fluorescence confocal images of TJP1 staining by manually outlining each cell staining. An average of 250 cells from multiple pictures of different WT ( n = 2) and mut OVECs ( n = 2) were analysed. 2.11 BRCA1 silencing BRCA1 silencing was performed as described in ( 20 ). 2.12 Proliferation and apoptosis assay Proliferation assay was performed as described in ( 21 ). Cells were stimulated with 20% HS, 10 ng/mL TGFβ (Prepotech), or 500 pg/mL and 2500 pg/mL17-β estradiol for 24h. After 3h, the absorbance was read using the Spark Multimode Microplate Reader (Tecan) (492 nm). Apoptosis assay was performed as described in ( 22 ). Cells were stimulated with 10 ng/ml TGFβ, and 500 µM H 2 O 2 as a positive control. 2.11. Evaluation of real-time cell growth using a microfluidic device Detailed information on vessel quantification is provided in Supplementary Information . 2.13 EndMT To evaluate the EndMT, the protocol described in ( 23 ) was adapted using 10 ng/ml TGFβ. At 48h after stimulation with TGFβ, images were acquired to evaluate cell morphology, and the cells were lysed with Buffer RL (Norgen Biotek) to proceed with RNA extraction. 2.14. RNA isolation and Real-Time quantitative PCR RNA isolation and Real-Time quantitative PCR (RT-qPCR) were performed as described in ( 24 ). The expression level of the gene of interest was assessed by calculating the Cycle threshold (Ct) value for each gene, which was then normalized against the Ct value of a housekeeping gene (GAPDH or TBP), using the ΔCt or ΔΔCt methods. 2.15 Statistical analysis Statistical analysis was performed using GraphPad Prism 10.0. Differences between groups were assessed using either a paired Student’s t-test or a non-parametric signed-rank Wilcoxon test, based on data distribution. The normality of the data was evaluated both visually and with the Kolmogorov-Smirnov test. p < 0.05 was considered statistically significant. 3 Results 3.1. Characterization of ovarian vasculature and endothelial cells in BRCA1-mut vs WT patients Ovary specimens were obtained from healthy patients ( n = 9, categorized as WT) undergoing oophorectomy either due to Gender Dysphoria (GD; n = 6) or for other gynaecological conditions ( e.g. , paraovarian fibroma, endometrial hyperplasia; n = 3). Ovarian biopsies were obtained from women carrying the BRCA1 mutation during preventive oophorectomy ( n = 10, classified as mut) ( Fig. 1A ). The mean age of the WT group is 36.6 years (SD ± 13), while for the mut group is 42 years (SD ± 11). We initially investigated differences in vascularization between WT and mut ovarian tissues. IHC analysis involving CD34 ( Fig. 1B,C ) and CD31 ( Fig. 1D,E ) staining, along with quantification using QuPath, revealed no significant differences in the number of CD31 + mature vessels ( Fig. 1E ), but a higher number of CD34 + newly formed vessels in the mut tissue ( Fig. 1C ). To investigate whether the differences in vascularization were due to different angiogenic behaviours, we isolated ECs from both types of tissues, successfully obtaining pure EC populations ( Fig. 1F ). The cells were found to be nearly 100% positive for CD31 (vascular endothelium marker), approximately 3–4% positive for LYVE1 (lymphatic endothelial marker), and 10–15% positive for CD90 (pericyte marker). Additionally, we confirmed a reduced expression of the BRCA1 gene and protein in mut compared to WT cells ( Supplementary Fig. S1 ). Further expansion of these cells revealed both morphological ( Fig. 1G ) and growth differences, prompting us to investigate these findings in greater depth. 3.2. RNA-Seq for uncovering differential gene expression between WT and BRCA1-mut OVECs To gain a broad view of the potential differences between the two endothelial cell populations, we conducted a transcriptome analysis. RNA sequencing analysis was conducted on 6 WT versus 6 mut OVEC populations to identify DETs between the two groups. PCA clustered the samples into two clearly distinct groups: WT and BRCA1-mut cells ( Fig. 2A ). Differential gene expression analyses identified 11547 differentially transcripts, including 694 up-regulated and 6239 down-regulated transcripts (|log2 fold change|>1, p-value adj < 0.05). As shown in Fig. 2B , the differential transcripts are consistent across the samples when comparing the two different biological groups. Given the elevated number of down-regulated transcripts, we decided to focus on the canonical transcripts as annotated in Ensembl database, thus obtaining 76 up-regulated and 1253 down-regulated DETs ( Supplementary Table S2 ). To perform functional annotation of the gene lists we utilized Qiagen IPA, obtaining disregulated Disease/Functions, the Canonical Pathways, Upstream Regulators, and Regulators Effects ( Supplementary Table S3 ). Several pathways related to cell proliferation, viability and angiogenesis are predicted as differentially regulated, as well as elements related to cell movements and migration ( Fig. 2C ; Supplementary Table S3 ), such as RHO GTPase cycle, Stahmin1, PTEN signalling, and RHODI signalling. For the regulatory aspect ( Fig. 2D ), several transcription factors related to cell proliferation and migration, like FOXM1 and HGF, and their related target downregulation may have a critical role in the observed phenotypes. We next compared our differentially expressed gene list against the repository of published signatures on the Rummagene website to benchmark our results against existing studies. Thus, we identified 478 individual publication-derived gene sets (adjusted p-value < 0.05), 27 of which are annotated to epithelial–mesenchymal transition. Among these gene sets, EndMT-associated transcriptional signatures, as described in PMC6291168, PMC7080988, and PMC11841332 ( 15 – 17 ), were systematically interrogated and were found to display consistently higher scores in WT compared to mut OVECs ( Fig. 2E, Supplementary Table S4 ). 3.3. BRCA1-mut OVECs express a lower angiogenic receptor signature but exhibit a stronger intrinsic functional angiogenic behaviour Based on RNA-Seq results ( Supplementary Fig. S2 ) , we aimed to evaluate angiogenic responses. First, we confirmed the differential expression of angiogenic receptors ( Fig. 3 ), with an overall lower expression in mut cells, except for NRP1 and PDGFRA ( Fig. 3A-G ). This pattern was confirmed by a statistically significant reduction of the "angiogenic signature" ( Fig. 3H ). No differences were found in soluble factors ( Supplementary Fig. S3 ). Functional assay revealed significant differences in migration ( Fig. 3I ), tube formation ( Fig. 3J ), and viability ( Fig. 3K ) between the two groups, most pronounced under serum deprivation (SFM). In this context, the mut cells consistently displayed enhanced angiogenic activity, highlighting their increased pro-angiogenic potential. Cell proliferation was investigated by cell count after 24h of culture in the presence or absence of serum ( Fig. 3L ). Additionally, we utilized an innovative real-time cell growth prototype (“TICheP system”), confirming that the mut OVECs reached confluence more rapidly ( Fig. 3M-O ). Since oxygen deficiency frequently occurs in the tumour microenvironment, we investigated whether mut OVECs respond differently to hypoxia. Thus, we tested cell viability in SFM under normoxia and at an oxygen partial pressure (pO 2 ) of 2%. No differences in viability were observed between mut and WT cells, although activation of the hypoxic pathway was confirmed by VEGF upregulation ( Supplementary Fig. S4 ). 3.4. Gene network analysis indicative of angiogenic properties dysregulation in BRCA1-mut OVECs Upstream regulator analysis performed identified putative upstream molecules, including transcription factors, enzymes, and soluble factors, that are predicted to be activated or inhibited and may account for the transcriptional changes detected in our dataset. The analysis of differentially expressed genes in mut OVECs revealed distinct regulatory networks potentially underlying the altered angiogenic phenotype. Notably, LAMA4 emerged as an activated upstream regulator (Z-score = 3.1), whereas both HGF and VEGF were predicted to be inhibited (Z-scores=–5.3 and − 5.5, respectively) ( Fig. 4 ; Supplementary Table S3 ). A closer inspection of the mechanistic networks highlighted specific regulatory connections for LAMA4, HGF, and VEGFA family members. The LAMA4 network displayed multiple downstream targets involved in extracellular matrix organization and cell adhesion (ITGA8, LAMC1, and LAMC3), cell migration (TMOD1), and angiogenic signalling (FLT1, LIFR, PLXND1, and TGFBR2), consistent with its established role in vascular basement membrane dynamics. Conversely, the HGF and VEGFA family networks appeared broadly inhibited, with reduced activation of canonical angiogenic pathways, including EFNB2, EPHA4, ITGA2, ITGA6, and ITPR3. 3.5. BRCA1-mut OVECs have altered adhesion molecule expression and form unfunctional monolayers. As RNA-Seq analysis revealed altered signatures in cell-cell adhesion molecules and EndMT ( Fig. 2E, Supplementary Fig. S5) , we analyzed the presence and expression of adhesion molecules and endothelial markers (CD31, VE-cadherin, vWF, and TJP1, Fig. 5A ). The cellular architecture differences observed under the inverted microscope were confirmed by IFs, although no evident differences emerged in terms of protein expression ( Fig. 5B ; Supplementary Fig. S6 ). We then assessed cell perimeter, Feret diameter, circularity, and roundness through confocal images of TJP1. In the context of ECs, polygonal ECs exhibit circularity and roundness values close to 1, whereas values close to 0 indicate elongated shapes. We demonstrated that mut OVECs displayed significantly higher values of cell perimeter and Feret diameter ( Fig. 5C,D ), and lower circularity and roundness ( Fig. 5E,F ) compared to WT OVECs. We further analysed endothelial ( PECAM1 , CDH5 , VWF , and TJP1 ) and mesenchymal ( FN1 , and COL1A2 ) markers through RT-qPCR. We confirmed RNA-Seq results, observing higher expression of endothelial markers in WT cells ( Fig. 5G-J ), as supported by the EC signature ( Fig. 5K ). In contrast, the expression of mesenchymal markers did not differ significantly, although their levels were generally lower in WT populations ( Fig. 5L,M ), particularly when considering the endothelial to mesenchymal marker ratio ( Fig. 5N ). The reduced gene expression of CD31/PECAM-1 was validated at protein level using flow cytometry ( Fig. 5O-P ). To investigate whether changes in adhesion molecule expression affect permeability, we performed leakage assays using the transwell (TW) model ( 25 ). OVECs were grown to confluence in the upper chamber of TWs, and treated with Histamine (HIS) or Bradykinin (BK) to assess vascular permeability. FITC-conjugated BSA was added to the upper chamber, and fluorescence in the lower chamber was evaluated after 5, 15, and 30 min. As shown in Fig. 5Q , the permeability of mut OVECs was more variable compared to WT OVECs in resting conditions. Furthermore, the response of mut OVECs to vasoactive stimuli was more intense and constant. 3.6. BRCA1-mut OVECs are more prone to EndMT transition. The enrichment of EndMT-related signatures observed by Rummagene analysis provides strong external validation and is entirely consistent with the mesenchymal phenotypic changes we observe in our model ( Fig. 2E, Supplementary Fig. S5) . To deepen our understanding of EndMT, we evaluated the expression of some known EndMT markers using RT-qPCR 48h after TGFβ treatment. If EndMT has occurred, the expression levels of endothelial markers are expected to decrease, while mesenchymal markers increase. As we observed cell suffering in WT OVECs after TGFβ treatment, cell viability and apoptosis were investigated, but no effects were observed ( Supplementary Fig. S7 ). Conversely, a morphological evaluation revealed distinct differences following TGFβ treatment: WT cells maintained a rounded and regular shape, while mut cells exacerbated their elongated/disorganized appearance ( Fig. 6A ). The treatment with TGFβ induced a stronger mesenchymal transition in mut OVECs, significantly downregulating the endothelial gene signature ( Fig. 6B ). We conducted then confocal IF analysis, which demonstrated considerable alterations in morphological regularity and cytoskeletal organization in resting mut OVECs ( Fig. 6C ), differences exacerbated after TGFβ stimulation. 3.7. BRCA1 silencing induces angiogenetic modulation only in serum-free conditions. To better understand the relationship between phenotype and BRCA1 mutation, BRCA1 expression was silenced in HUVECs. This approach developed a robust cellular model as an alternative to OVECs, due to the challenges of obtaining healthy ovary tissue. We silenced a pool from 4 different HUVECs to minimize individual variability. The cells were cultured in a serum-free (REST) or complete medium (FBS), after which experiments were conducted ( Fig. 7A,B and Supplementary Fig. S8 ). We achieved approximately 50% silencing of BRCA1 expression, which mimicked the heterozygosity of mut OVECs ( Fig. 7C ). Then, we analysed the expression of endothelial markers ( Fig. 7A ) and angiogenic receptors ( Fig. 7B ) using RT-qPCR. Notably, in the presence of serum, we failed to observe differences ( Supplementary Fig. S8 ). Similarly to OVECs, some differences in gene expression emerged in the absence of serum. Indeed, we observed the downregulation of several genes ( i.e. , CDH5 , TJP1 , KDR , and FGFR1 ) in BRCA1-silenced cells compared to the control. For the other genes, no significant changes were observed. Conversely, while no statistical differences were found in the leakage assay, great variability in permeability responses was noted in ECs with lower BRCA1 expression, as observed in OVECs. Surprisingly, siBRCA1 HUVECs displayed a lower proliferative/migratory rate in scratch assays with 20% FBS, likely due to the lower expression of angiogenic receptors ( Fig. 7E,F ). On the contrary, the real-time proliferation assay using the “TiCheP prototype” confirmed that BRCA1-silenced HUVECs reached confluence more rapidly ( Supplementary Fig. S9 ). 3.8. The dysfunctional behaviour of heterozygous ECs is tissue-specific. Finally, to understand if the tissue district was able to influence the cell behaviour, we compared ADMECs of mut vs WT patients. First, we investigated endothelial markers ( Fig. 8A ), which were downregulated in mut OVECs. In mut ADMECs, we did not observe a reduced expression of endothelial marker transcripts ( Fig. 8B ), nor of CD31/PECAM-1 by flow cytometry ( Fig. 8C ). However, our hypothesis that the differing behaviour of the endothelium of the two tissue districts was due to varying sensitivity to oestrogen was challenged by the finding that mut OVECs did not respond to oestrogen stimulation ( Supplementary Fig. S10A ), although they express the receptors, unlike ADMECs ( Supplementary Fig. S10B ). Discussion A considerable body of research has explored the role of BRCA1 depletion in cancer development. While extensive investigations have focused on breast cancer and other conditions in which BRCA1 function is entirely lost, the functional alterations associated with heterozygous BRCA1 status remain unclear. An example is a recent study that analysed whole exome sequencing, RNA-seq, and proteomic data of over 100 human Fallopian tube tissues, finding minimal differences between BRCA1/2 carriers and non-carriers prior to loss of heterozygosity (7). Limited information is available regarding the implications of this condition in ovarian tissue, particularly in ECs. One of the earliest studies connecting BRCA1 and endothelial behaviour highlighted a previously unrecognized role of BRCA1 as a gatekeeper of EC survival using an EC-specific BRCA-KO model (26). BRCA1-mutated individuals have fewer mature oocytes after ovarian stimulation and a reduced follicle reserve. Follicle survival and maturation are tied to ovarian angiogenesis (1). The observations regarding the connection between the BRCA1 mutation and angiogenesis have an indirect feature. In breast and pancreatic cancers, BRCA1 mutations can promote angiogenesis by affecting CAFs, leading to tumour-supporting inflammation and blood vessel growth (27). An interaction between BRCA1 and HIF-1α was found in human breast cancer, where hypoxia-stimulated VEGF promoter activity and secretion were reduced in BRCA1-silenced cells (27). Danza and colleagues emphasized the role of miR-578 and miR-573 (upregulated in mut breast cancer tissue) in regulating BRCA 1/2-related angiogenesis by targeting key regulators of focal adhesion, VEGF, and HIF-1 signalling pathways (28). Apart from VEGF, it is postulated that BRCA1 can affect other pro-angiogenic factors, particularly angiopoietin-1, by forming a repressive complex with C-terminal binding protein-interacting protein (CtIP) and zinc finger and BRCA1-interacting protein with KRAB domain-1 (ZBRK1), which then inhibits the expression of angiopoietin-1 (29). Our observations showed that mut OVECs exhibited lower expression levels of angiogenic receptors, while we did not observe differences in growth factors ( e.g ., VEGF), except for angiopoietin 1, consistently with Furuta et al. (29). The observed decreased expression of angiogenic receptors, which may appear paradoxical in a tumour context, is consistent with the functional behaviour of the cells under SFM conditions. Specifically, mut OVECs exhibited greater angiogenic activity than WT OVECs only under SFM, indicating that the mutation confers an intrinsic capacity for proliferation, migration, and neovascularization that is less reliant on exogenous microenvironmental cues. This interpretation is further supported by the IPA network, which revealed inhibition of both HGF and VEGFA signalling alongside activation of LAMA4. Together, these findings point to a shift from classical growth factor-dependent angiogenesis toward a compensatory mechanism based on extracellular matrix remodelling. In addition, our results indicate that mut OVECs undergo morphological and molecular changes consistent with a partial EndMT. The loss of endothelial integrity, heightened response to vasoactive stimuli, and enrichment of EndMT-related gene signatures suggest that BRCA1 deficiency increases endothelial plasticity and susceptibility to pro-mesenchymal cues. Such vascular alterations may create a permissive microenvironment that facilitates early tumour development and could serve as potential biomarkers or therapeutic targets in BRCA1-associated diseases. BRCA1 inhibits VEGF gene transcription via ER-α (30), and this connection could explain the tissue-specificity observed. The behaviour of BRCA1-silenced HUVECs did not fully correspond to that observed in mut OVECs. This discrepancy is likely attributable to the fact that "acute" silencing may not evoke the same biological effects as "chronic" gene deficiency. Additionally, the diminished expression of hormone receptors in HUVECs could lead to behaviour akin to that observed by ADMECs. Our study is the first to reveal a significant alteration in the vascular density of newly formed vessels (CD34 + ) within the ovarian tissue of women carrying the BRCA1 mutation. Since 1997, a correlation between colour Doppler ultrasound and microvessel density in detecting angiogenetic differences between benign and malignant ovarian tumours has been demonstrated (31, 32). This finding raises the possibility that in vivo imaging modalities capable of assessing tissue vascularization, such as Doppler ultrasound, could be harnessed to enhance screening and early detection strategies for individuals with this genetic predisposition. One limitation of this study is the low number of populations examined since the challenges of obtaining biopsies from healthy ovaries, particularly WT, often precluded the attainment of robust statistical significance; the small size of biopsies hindered the isolation of a sufficient number of cells, requiring the in vitro expansion of OVECs. Moreover, by deliberate choice, we did not utilize cells beyond the fifth passage to maintain intact endothelial features. Nonetheless, the results of this study are corroborated by multiple lines of evidence gathered through various experimental methods, all leading to the same conclusion. Certainly, the key findings of this study indicate that both in vitro and ex vivo evaluations demonstrate endothelial dysfunction in ovarian tissue associated with the heterozygous mutation, which could be considered a new diagnostic and therapeutic target for further investigation. Declarations Ethics approval and consent to participate The study was reviewed and approved by the Regional Ethical Committee of FVG (CEUR, Udine, Italy; prot. 0010144/P/GEN/ARCS 2019). Authorship contribution statement Silvia Pegoraro, Barbara Fogar and Mariagiulia Spazzapan : Writing – review & editing; Writing – original draft; Visualization; Validation; Software; Methodology; Investigation; Formal analysis; Data curation; Conceptualization. Giulia Canarutto : Visualization; Validation; Software; Methodology; Investigation; Formal analysis; Data curation; Writing – original draft. Andrea Balduit : Methodology; Data curation; Investigation; Writing – review & editing. Formal analysis. Gabriella Zito : Resources; Data curation; Formal analysis. Alessandro Mangogna : Data curation; Formal analysis; Writing – original draft. Miriam Toffoli : Investigation; Methodology; Data curation. Giovanni Papa : Resource; Validation. Luca Spazzapan : Resource. Francesca Rossi : Methodology. Eva Andreuzzi : Data curation; Writing – review & editing Federico Romano : Resource. Silvano Piazza : Visualization; Software; Methodology; Investigation; Formal analysis; Data curation; Writing – original draft. Chiara Agostinis : Conceptualization, Investigation; Data curation; Formal analysis; Writing – review & editing; Writing – original draft; Project administration. Giuseppe Ricci : Conceptualization; Editing; Formal analysis; Funding acquisition; Writing – review & editing. Roberta Bulla : Conceptualization; Supervision; Funding acquisition; Resources; Writing - review & editing. DATA AVAILABILITY All the raw RNA-seq data generated from this study have been uploaded to the NCBI Gene Expression Omnibus (GEO), and the accession number is GSE308525. Acknowledgements We thank Dr Martina Palmieri and Giorgia Meshini for help with patient enrolment, Dr Roberto Carrano for real-time cell growth experiments using a microfluidic device. Declaration of competing interest The authors declare no competing interests. Funding This work was supported by the Ministry of Health, Rome - Italy, in collaboration with the Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste – Italy (RC23/18 and RC20/23 to G.R.) and was funded by European Union - Next Generation EU (D40-RPRIN22BULLA_01). References Zhang X, Niu J, Che T, Zhu Y, Zhang H, Qu J. 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Additional Declarations There is no duality of interest Supplementary Files Suppl.TableS3.xlsx Supplemental Table S3 Suppl.TableS5.xlsx Supplemental Table S5 Suppl.TableS4.xlsx Supplemental Table S4 SupplementalMaterial.docx Supplemental Material Graphicalabstract.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. 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1","display":"","copyAsset":false,"role":"figure","size":6309505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eGraphical representation of the two cohorts of patients enrolled in the study, along with the samples collected. (\u003cstrong\u003eB,D\u003c/strong\u003e) Representative images from ovarian tissue samples (\u003cem\u003en\u003c/em\u003e = 5 WT, \u003cem\u003en\u003c/em\u003e = 5 mut) showing IHC analysis of cortical ovarian tissue derived from either WT or mut patients. Vessels were visualised using staining for CD34 (\u003cstrong\u003eB\u003c/strong\u003e) or CD31 (\u003cstrong\u003eD\u003c/strong\u003e), employing mouse anti-human CD34 and rabbit anti-human CD31 antibodies, respectively. The AEC chromogen (red) was used to visualize the binding of antibodies, while nuclei were stained with Mayer’s Haematoxylin. For each sample, from 5 to 7 cropped regions were extracted from whole-slide images of both WT and mut patients. Images were used for quantifying vascular density by QuPath software (as shown by the graphs in \u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eE\u003c/strong\u003e). Scale bar 50 µm. (\u003cstrong\u003eF\u003c/strong\u003e) Mutated OVECs were characterised through cytofluorimetry using a panel of directly conjugated antibodies to assess extracellular markers (CD31, LYVE1, and CD90). The representative results of these cytofluorimetric analyses indicate the percentage of cytofluorimetric analyses indicate the percentage of cell positivity. (\u003cstrong\u003eG\u003c/strong\u003e) Bright field images of mut (up) or WT (down) OVECs. Original magnification 10X, Scale bar 100 µm.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/7b7db055a049774cc6a71d81.png"},{"id":97441247,"identity":"37c1c182-8e28-4211-9fc9-5a3997c2c32b","added_by":"auto","created_at":"2025-12-04 12:03:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1247131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNA-seq analysis of wild-type (WT) vs BRCA1-mutated ovarian endothelial cells (OVECs).\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Principal Component Analysis (PCA). Scatterplot of the first two principal components of the transcriptome profiles from WT (black) and BRCA1-mutated OVECs (pink). The two groups separate clearly along PC1, indicating a strong global transcriptional divergence. Axes represent the proportion of variance explained by each component. (\u003cstrong\u003eB\u003c/strong\u003e) Heatmap of differentially expressed transcripts (DETs). Hierarchical clustering of differentially express transcripts (DETs adj. p \u0026lt; 0.05, |log₂FC| \u0026gt; 1) between WT and BRCA1-mut OVECs. Each column corresponds to a sample and each row to a transcript. Expression values are shown as Z-scores (colour scale: blue = downregulated, white = mean expression, red = upregulated). WT and BRCA1-mut samples form distinct clusters. (\u003cstrong\u003eC\u003c/strong\u003e) Functional annotation (IPA). Barplot of the top 20 Diseases and Functions predicted by Ingenuity Pathway Analysis. Bars are color-coded by FDR adjusted p-value derived score (-log10(P-value) ). Functions related to cell proliferation, viability, and migration are enriched in BRCA1-mut cells. The upper panel shows enriched \u003cem\u003eDiseases and Functions\u003c/em\u003e, while the lower panel displays enriched \u003cem\u003eCanonical Pathways\u003c/em\u003e. (\u003cstrong\u003eD\u003c/strong\u003e) Upstream regulator analysis. Heatmap of the predicted activity of upstream regulators. Each column represents an individual sample and each row a predicted regulator. Z-scores are color-coded (blue = predicted inhibition, white = no change, red = predicted activation). Regulators associated with proliferation and angiogenesis (FOXM1, HGF, SP1, STAT1) are predicted to be inhibited in BRCA1-mut OVECs, while regulators such as LAMA4 are predicted to be activated. (\u003cstrong\u003eE\u003c/strong\u003e) Violin plots of EndMT-related gene signature enrichments derived using Rummagene.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/5a68edb6745547ecdf6d1e25.png"},{"id":97441251,"identity":"3b238383-70ac-48bd-af27-611881c8eb2b","added_by":"auto","created_at":"2025-12-04 12:03:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2618253,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA-G\u003c/strong\u003e). RT-qPCR of angiogenic receptor genes \u003cem\u003eKDR\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e), \u003cem\u003eFLT1\u003c/em\u003e (\u003cstrong\u003eB\u003c/strong\u003e), \u003cem\u003eTEK\u003c/em\u003e (\u003cstrong\u003eC\u003c/strong\u003e), \u003cem\u003eFGFR1\u003c/em\u003e (\u003cstrong\u003eD\u003c/strong\u003e), \u003cem\u003eNRP1\u003c/em\u003e (\u003cstrong\u003eE\u003c/strong\u003e), \u003cem\u003eNRP2 \u003c/em\u003e(\u003cstrong\u003eF\u003c/strong\u003e), \u003cem\u003ePDGFRA\u003c/em\u003e (\u003cstrong\u003eG\u003c/strong\u003e), expressed by WT \u003cem\u003evs\u003c/em\u003e mut OVECs. Gene expression levels were calculated using the ΔCt method, normalizing to the TBP housekeeping gene. Data are expressed as individual values along with mean ± SEM for 4 WT and 6 mut OVEC populations. *p \u0026lt; 0.05 (Mann-Whitney one-tailed test). (\u003cstrong\u003eH\u003c/strong\u003e) Analysis of the angiogenic receptor signature was performed by combining the results of the 2^-ΔCt average for each receptor investigated by RT-qPCR in WT \u003cem\u003evs\u003c/em\u003e mut OVECs. *p \u0026lt; 0.05 (paired two-tailed t-test). (\u003cstrong\u003eI\u003c/strong\u003e) Migration assays were performed using a transwell system. WT or mut OVECs were stained with FAST DiI™, seeded in FluoroBlok™ Inserts (1.5x10\u003csup\u003e5\u003c/sup\u003e cells/insert), and the lower chamber was loaded with 20% of NHS as a chemoattractant stimulus or left without stimuli (SFM). After 24h, fluorescence was measured using the Spark Microplate Reader (Tecan). The percentage was calculated based on a standard curve as a reference. Data are expressed as mean ± standard deviation (SD). (\u003cstrong\u003eJ\u003c/strong\u003e) Tube formation assays were performed using WT or mut OVECs seeded onto Matrigel® in 8-chamber slides. The cells were stimulated with 20% NHS or left in SFM. After 18h, the capillary-like structures formed by ECs were manually counted using under a TiEsseLab BDS 600 microscope, comparing the different conditions. Data are expressed as mean ± SD. **p \u0026lt; 0.01. (\u003cstrong\u003eK\u003c/strong\u003e) Viability assays were performed using the MTS assay on WT and mut OVECs, stimulated or not (SFM) with 20% NHS for 24h. Three different populations of WT and mut OVECs were analysed. The data are presented as mean ± SEM and normalized to WT OVECs. (\u003cstrong\u003eL\u003c/strong\u003e) Proliferation of WT and mut OVECs was assessed through cell counting using a particle counter Coulter Z1. Two distinct populations of WT and mut OVECs were analysed. The data are presented as mean ± SEM of cell numbers. (\u003cstrong\u003eM,N\u003c/strong\u003e) OVECs were seeded into the chip wells at a concentration of 350 cells/μL and left to adhere in an incubator for 12 to 16h. Subsequently, the cells were subjected to serum starvation using a constant flow rate of 4.09 μL/min for 30 min. After 24h and 48h of starvation, the cell confluence curve and the relative growth of WT and mut cells were evaluated. Data are expressed as mean ± SD. (\u003cstrong\u003eO\u003c/strong\u003e) Representative pictures of WT and mut OVECs analysed in (\u003cstrong\u003eM\u003c/strong\u003e) at three different time points (0, 24, and 48h). Scale bar 100 µm.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/180f24e70f4ebdbc551326eb.png"},{"id":97667537,"identity":"55c2cbdd-9f13-472c-a2f8-fe23db26e4f5","added_by":"auto","created_at":"2025-12-08 09:23:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5903029,"visible":true,"origin":"","legend":"\u003cp\u003eIPA gene interaction network diagram. This map predicts mechanistic regulatory signalling networks affected in mut OVECs. Figure legend on the right illustrates the relationship between molecules within the network.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/85ae2a5e7ccca424056ca65f.png"},{"id":97441260,"identity":"e28bffc5-4edd-4b3b-af9f-80cb85920734","added_by":"auto","created_at":"2025-12-04 12:03:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6482162,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic representation of adhesion molecule organization in endothelial cells (ECs) created by Biorender. The cell-cell adhesion molecules, as VE-cadherin and CD31/PECAM-1, also serve as markers for ECs. (\u003cstrong\u003eB\u003c/strong\u003e) Representative images obtained from confocal microscopy illustrating the characterization for endothelial markers and comparison of mut \u003cem\u003evs\u003c/em\u003e WT OVECs. ECs were stained in red for CD31/PECAM-1, VE-cadherin, vWF, or TJP1, whereas the nuclei were stained blue with DAPI. Images were acquired using a Zeiss Lsm 900 confocal microscope and processed with ImageJ software. Scale bar, 20 µm. (\u003cstrong\u003eC-F\u003c/strong\u003e) Morphological quantitative analysis of cell morphology based on TJP1 staining, performed using ImageJ. The following parameters were measured: perimeter (µm) (\u003cstrong\u003eC\u003c/strong\u003e), Feret diameter (µm) (\u003cstrong\u003eD\u003c/strong\u003e), cell circularity (\u003cstrong\u003eE\u003c/strong\u003e), and roundness (\u003cstrong\u003eF\u003c/strong\u003e). (\u003cstrong\u003eG-N\u003c/strong\u003e) RT-qPCR analysis of endothelial (\u003cstrong\u003eG-J\u003c/strong\u003e) and mesenchymal (\u003cstrong\u003eL-M\u003c/strong\u003e) markers expressed by WT \u003cem\u003evs\u003c/em\u003e mut OVECs. The gene expression levels were calculated using the ΔCt method, normalizing to the TBP housekeeping gene. Data are expressed as the mean ± SEM of 4 WT and 6 mut OVEC populations. *p \u0026lt; 0.05 (unpaired one-tailed t-test). (\u003cstrong\u003eK\u003c/strong\u003e) Analysis of endothelial gene signature, combining the results of the 2^-ΔCt average of each endothelial marker investigated by RT-qPCR in WT \u003cem\u003evs\u003c/em\u003e mut OVECs. *p \u0026lt; 0.05 (paired one-tailed t-test). (\u003cstrong\u003eN\u003c/strong\u003e) Analysis of the ratio of the expression levels of mesenchymal (Mes) and endothelial (End) markers in mut \u003cem\u003evs\u003c/em\u003e WT OVECs. The data are processed based on RT-qPCR results presented in panels \u003cstrong\u003eG-J\u003c/strong\u003e and \u003cstrong\u003eL-M\u003c/strong\u003e. *p \u0026lt; 0.05 (unpaired two-tailed t-test). (\u003cstrong\u003eO-P\u003c/strong\u003e). Flow cytometry analysis of CD31 expression by WT \u003cem\u003evs\u003c/em\u003e mut OVEC. Cells were stained with an anti-CD31 Cy-7-conjugated antibody and analysed using the Attune\u003csup\u003eTM\u003c/sup\u003e NxT Flow Cytometer (Invitrogen). (\u003cstrong\u003eQ\u003c/strong\u003e) Permeability assay on WT and mut OVECs that were stimulated with bradykinin (BK), histamine (HIS), or maintained under resting conditions. Cell permeability was evaluated after 5, 15, and 30 min of stimulation by measuring the amount of FITC-labeled BSA that leaked through the monolayer of OVECs into the lower chamber. Data are expressed as the mean ± SEM of 3 WT and 3 mut OVEC populations.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/156b51dea162f130cbdd48f9.png"},{"id":97667080,"identity":"f60466d8-2175-452f-b3d2-780b9bd47f72","added_by":"auto","created_at":"2025-12-08 09:22:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7669361,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Representative images of phase contrast microscopy showing WT and mut OVECs treated with 10 ng/ml TGFβ or maintained under resting conditions. Arrows indicate cells exhibiting an elongated morphology. Scale bar 100µm. (\u003cstrong\u003eB\u003c/strong\u003e) Analysis of the ratio between TGFβ-treated and resting samples in relation to the endothelial gene signature. This analysis was conducted using the results of the 2^-ΔCt average of each endothelial marker investigated by RT-qPCR in 3 WT and 3 mut OVECs treated or not (Rest) with TGFβ. The gene expression levels of each endothelial marker (\u003cem\u003ePECAM1\u003c/em\u003e, \u003cem\u003eCDH5\u003c/em\u003e, \u003cem\u003eVWF\u003c/em\u003e, and \u003cem\u003eTJP1\u003c/em\u003e) were calculated with the ΔCt method, normalizing to the GADPH housekeeping gene. *p \u0026lt; 0.05 (Welch’s two-tailed t-test). (\u003cstrong\u003eC\u003c/strong\u003e) Representative images of confocal microscopy of TJP1, F-actin, and vimentin in mut and WT OVECs treated with TGFβ 10 ng/ml or maintained under resting conditions. Nuclei were stained in blue with DAPI. Images were acquired using the Zeiss Lsm 900 confocal microscope and processed with ImageJ software. Scale bar, 20µm.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/db615bb7ce46e65f854ec24d.png"},{"id":97667917,"identity":"e44f7cac-ce82-4472-ad7f-d8ee0d4232fb","added_by":"auto","created_at":"2025-12-08 09:24:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1815649,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) RT-qPCR on endothelial marker (\u003cstrong\u003eA\u003c/strong\u003e) and angiogenic receptor (\u003cstrong\u003eB\u003c/strong\u003e) genes in HUVECs, silenced or not with siRNA targeting BRCA1. The relative gene expression levels were calculated using the ΔΔCt method, comparing the siBRCA1 values to those of the siCTRL and normalizing to the GAPDH housekeeping gene. Data are expressed as the mean ± SEM (\u003cem\u003en\u003c/em\u003e= 6). *p \u0026lt; 0.05 (unpaired two-tailed t-test). (\u003cstrong\u003eC\u003c/strong\u003e)RT-qPCR to assess BRCA1 gene expression levels in HUVECs, silenced or not with siRNA targeting BRCA1, to confirm the effectiveness of BRCA1 silencing. The relative gene expression levels were calculated using the ΔΔCt method, comparing the siBRCA1 values to those of the siCTRL and normalizing to the GAPDH housekeeping gene. Data are expressed as the mean ± SEM \u003cem\u003e(n\u003c/em\u003e = 6). ***p \u0026lt; 0.001 (unpaired two-tailed t-test). (\u003cstrong\u003eD\u003c/strong\u003e) A permeability assay carried out on HUVECs, silenced or not with siRNA targeting BRCA1 for 72h, followed by stimulation with bradykinin (BK), histamine (HIS), or maintained under resting conditions. Cell permeability was evaluated after 5, 15, and 30 min of stimulation by measuring the amount of FITC-labelled BSA leaked through the monolayer of HUVEC cells into the lower chamber. Data are expressed as the mean ± SEM (\u003cem\u003en \u003c/em\u003e= 4). (\u003cstrong\u003eE\u003c/strong\u003e) A wound healing assay was performed using HUVECs (\u003cem\u003en\u003c/em\u003e = 4) after transfection with siBRCA1 (BRCA1 gene) or siCTRL for 48h. After scratching the centre of the endothelial monolayer, the cells were stimulated with 20% FBS. Images of the wound fields were captured at 0 and 10h, and the wound areas were measured using ImageJ software to calculate the percentage of wound closure. The percentage of wound healing is relative to the siCTRL in resting conditions (100% of wound healing). Data are expressed as mean ± SEM; **p \u0026lt; 0.01. (\u003cstrong\u003eF\u003c/strong\u003e) Representative images of the wound closure presented in (\u003cstrong\u003eE\u003c/strong\u003e). White dot lines were drawn to indicate the analysed wound area.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/af5f83b25c8b04587f704596.png"},{"id":97441269,"identity":"5313e2f9-c3a1-4f3f-a7bd-1068b1e48334","added_by":"auto","created_at":"2025-12-04 12:03:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1237676,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Representative images obtained from confocal microscopy illustrating the characterization of endothelial markers and comparison of mut vs WT ADMECs. ECs were stained in red for CD31/PECAM-1, VE-cadherin, vWF, or TJP1, whereas the nuclei were stained blue with DAPI. Images were acquired using a Zeiss Lsm 900 confocal microscope and processed with ImageJ software. Scale bar, 20 µm. (\u003cstrong\u003eB\u003c/strong\u003e) RT-qPCR analysis of endothelial markers expressed by WT vs mut ADMECs. The gene expression levels were calculated using the ΔCt method, normalizing to the TBP housekeeping gene. Data are expressed as the mean ± SEM of 3 WT and 3 mut OVEC populations. *p \u0026lt; 0.05 (unpaired one-tailed t-test). (\u003cstrong\u003eC\u003c/strong\u003e) Flow cytometry analysis of CD31 expression by WT vs mut ADMECs. Cells were stained with an anti-CD31 Cy-7-conjugated antibody and analysed using the AttuneTM NxT Flow Cytometer (Invitrogen).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/7b030e918a0ed1890dc92213.png"},{"id":99787752,"identity":"8ce269c6-740f-4984-95b0-17ebbb8d7243","added_by":"auto","created_at":"2026-01-08 12:29:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":35272124,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/a0f1dd17-b630-4f10-b50c-561538678908.pdf"},{"id":97441248,"identity":"465a897f-e65b-457a-aea0-cc37f343b993","added_by":"auto","created_at":"2025-12-04 12:03:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":183632,"visible":true,"origin":"","legend":"Supplemental Table S3","description":"","filename":"Suppl.TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/28a2c4cd813542c81c4e7679.xlsx"},{"id":97668584,"identity":"5aea52d4-04bf-4642-a740-73253bd251f9","added_by":"auto","created_at":"2025-12-08 09:25:50","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":290135,"visible":true,"origin":"","legend":"Supplemental Table S5","description":"","filename":"Suppl.TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/0678ab36aebb867cf6f15159.xlsx"},{"id":97441249,"identity":"d7a3b7d8-817c-4e21-ba69-da2c1e3bc717","added_by":"auto","created_at":"2025-12-04 12:03:35","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":753791,"visible":true,"origin":"","legend":"Supplemental Table S4","description":"","filename":"Suppl.TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/c3ee9b9e2ce0353ca7c6caac.xlsx"},{"id":97668592,"identity":"d17fa60a-8873-41dc-8139-3dd146165df0","added_by":"auto","created_at":"2025-12-08 09:25:50","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3977535,"visible":true,"origin":"","legend":"Supplemental Material","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/0bf9e5ccfb6fdd08c8f83347.docx"},{"id":97441258,"identity":"38f85080-7b32-420e-afb0-6ae0228594c1","added_by":"auto","created_at":"2025-12-04 12:03:35","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":356747,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-7980820/v1/1dec95144839beb97d9c52f4.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"The impact of heterozygous BRCA1 mutations on ovarian angiogenesis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eA considerable body of research has explored the role of BRCA1 depletion in cancer development, but limited information is available regarding the implications of the functional alterations associated with heterozygous BRCA1 status in ovarian tissue. Growing evidence has indicated that the BRCA1 mutation negatively affects the ovarian reserve of carriers and accelerates ovarian ageing, impacting both the quantity and quality of reproductive outcomes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is important to emphasize that the survival and maturation process of follicles is directly related to ovarian angiogenesis (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBRCA1 functions as a 'caretaker' of genome stability, influencing several crucial cellular processes (\u003cem\u003ee.g.\u003c/em\u003e, DNA damage repair, transcriptional regulation, ubiquitination, and cell-cycle control) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). BRCA1 participates in various signalling pathways, including Homologous Recombination, P13K/AKT, apoptosis, and interaction with microRNAs, that can either upregulate or inhibit its function (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Decreased expression of BRCA1 activates PI3K/AKT pathway, which further facilitates cell cycle progression (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The PI3K/AKT pathway promotes angiogenesis by activating key endothelial cell (EC) functions (\u003cem\u003ee.g.\u003c/em\u003e, migration and proliferation), in both normal blood vessel development and tumour growth. Activation occurs through signals from growth factors, including VEGF, and contributes to the production of factors (\u003cem\u003ee.g.\u003c/em\u003e, nitric oxide and angiopoietins) supporting new blood vessel formation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough several studies have clarified the pathogenetic mechanisms behind BRCA mutations, there is still a lack of analysis regarding the heterozygous BRCA1-mutated ovarian microenvironment. Given the high penetrance of BRCA1/2 mutations for breast and ovarian cancer risk (approximately 50%), research has focused on determining whether these germline mutations cause any genomic/epigenomic alterations before the loss of heterozygosity, particularly in epithelium of disease-relevant tissue types [\u003cem\u003ei.e.\u003c/em\u003e, the Fallopian tubes (FT) and breast] (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe interaction between BRCA1-deficient tumour cells and the surrounding stroma promotes processes like EC survival and angiogenesis. As the tumour advances, angiogenesis becomes crucial, often leading to a transformation in which ECs undergo the endothelial-to-mesenchymal transition (EndMT) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). EndMT is characterized by a loss of endothelial markers and the acquisition of mesenchymal/fibroblastic phenotypes. Research targeting these molecules has made anti-angiogenic and anti-EndMT therapies a promising approach in tumours (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), including ovarian cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo date, no direct evidence exists linking heterozygous mutations of BRCA1 to altered behaviour of ECs in the pre-neoplastic microenvironment. This study aimed to explore the impact of BRCA1 mutations on ovarian stroma, particularly focusing on ECs and their potential contribution to tumorigenesis. We investigated the differences in angiogenic behaviour, susceptibility to EndMT, phenotypic plasticity, and gene expression profiles between ovarian ECs (OVECs) isolated from patients with (mut) and without BRCA1 (WT) mutations. Moreover, we explored whether vascularization in mutated ovaries is different.\u003c/p\u003e\u003cp\u003eOur novel findings may aid in creating effective early detection strategies for prevention of BRCA1 ovarian neoplastic transformation.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Patient enrolment\u003c/h2\u003e\u003cp\u003eDetailed information is provided in the \u003cb\u003eSupplementary Information\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Cell isolation and culture\u003c/h2\u003e\u003cp\u003eOVECs were isolated and maintained as described by Agostinis \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Human Umbilical Vein Endothelial Cells (HUVECs) were isolated and maintained according to Jaffe \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Human Adult Dermal Microvascular Endothelial Cells (ADMECs) were obtained from skin biopsies of patients undergoing reductive plastic surgery, as described by Agostinis \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Bioinformatics analyses\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 RNA-seq data preprocessing\u003c/h2\u003e\u003cp\u003eRaw paired-end FASTQ files were quality-checked using \u003cem\u003eFastQC\u003c/em\u003e (v0.11.9). Adapter sequences and low-quality bases were trimmed as required. Transcript-level quantification was performed with \u003cem\u003eSalmon\u003c/em\u003e (v1.9.0) in quasi-mapping mode using the Ensembl GRCh38 reference transcriptome (release 109; Homo_sapiens.GRCh38_decoy.index). Salmon was run with sequence bias and GC bias correction enabled. Quantification outputs were imported into R (v4.3.2) using the \u003cem\u003etximport\u003c/em\u003e package, and transcript counts were summarized at the gene level using the Ensembl v109 annotation (GTF).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Normalization and differential expression analysis\u003c/h2\u003e\u003cp\u003eRaw count matrices were filtered to remove lowly expressed features. Counts were normalized by size factors estimated with the \u003cem\u003eDESeq2\u003c/em\u003e package (v1.40). Differential expression analyses compared BRCA1-mutated (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) vs WT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) OVECs. Differentially expressed transcripts (DETs) were defined by an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Benjamini\u0026ndash;Hochberg correction) and absolute value of log₂ fold change\u0026thinsp;\u0026gt;\u0026thinsp;1. Principal Component Analysis (PCA) was performed on variance-stabilized counts using \u003cem\u003eDESeq2\u003c/em\u003e. Heatmaps were generated with the ComplexHeatmap package, and Z-scores were computed by row-standardization of normalized counts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Functional and regulator analyses\u003c/h2\u003e\u003cp\u003eFunctional annotation of DETs was performed with Ingenuity Pathway Analysis (IPA, Qiagen). Categories of Diseases and Functions and Canonical Pathways were ranked by \u0026ndash;log₁₀ p-value and visualized with barplots with the ggpubr package. Upstream regulator analysis and regulator effect prediction were carried out in IPA, and predicted activation states were represented as Z-scores (blue\u0026thinsp;=\u0026thinsp;inhibition, red\u0026thinsp;=\u0026thinsp;activation). Networks were exported from IPA to visualize relationships between upstream transcription factors, signalling molecules, and DETs via Cytoscape software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Signature benchmarking\u003c/h2\u003e\u003cp\u003eDET list was compared against a repository of 478 literature-derived gene sets using Rummagene. Signature enrichment was evaluated using the singscore method and visualization performed with ggpubr package. Significant enrichments were defined as FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. EndMT-related signatures were specifically examined, including those from (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.5 Software and reproducibility\u003c/h2\u003e\u003cp\u003eAll analyses were performed in R (v4.3.2) using Bioconductor packages, with code executed under Linux. Custom R scripts for QC, normalization, DET detection, and visualization are available upon request.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Angiogenesis assays\u003c/h2\u003e\u003cp\u003eMigration and wound healing assays were performed as described in (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Cells were stimulated with 20% human serum (HS) as positive control. Tube formation assay was performed as described in (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Proliferation assay was performed seeding 7x10\u003csup\u003e3\u003c/sup\u003e ECs/well in a 96-well plate, then stimulating them with TGFβ (10 ng/mL) or 20% HS as positive control. After 24h, MTS was added to each well, and cell proliferation was measured using Spark Plate Reader (Tecan) (450 nm).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Antibodies\u003c/h2\u003e\u003cp\u003eAntibodies used in immunohistochemistry (IHC), immunofluorescence (IF), and flow cytometry analyses, are listed in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Histo- and Immunohistochemical analysis for vessel identification\u003c/h2\u003e\u003cp\u003eImmunohistochemical analysis was performed as described in (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.7 QuPath Vessel quantification\u003c/h2\u003e\u003cp\u003eDetailed information is provided in \u003cb\u003eSupplementary Information\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Flow cytometry\u003c/h2\u003e\u003cp\u003eFlow cytometry analysis was performed as described in (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Immunofluorescence\u003c/h2\u003e\u003cp\u003eImmunofluorescence staining was performed as described in (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Morphological analysis\u003c/h2\u003e\u003cp\u003eMorphological analysis was performed using ImageJ software. Morphometric values of circularity, roundness (or aspect ratio, AR), Feret diameter, and perimeter of the cells were obtained from fluorescence confocal images of TJP1 staining by manually outlining each cell staining. An average of 250 cells from multiple pictures of different WT (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) and mut OVECs (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) were analysed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.11 BRCA1 silencing\u003c/h2\u003e\u003cp\u003eBRCA1 silencing was performed as described in (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Proliferation and apoptosis assay\u003c/h2\u003e\u003cp\u003eProliferation assay was performed as described in (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Cells were stimulated with 20% HS, 10 ng/mL TGFβ (Prepotech), or 500 pg/mL and 2500 pg/mL17-β estradiol for 24h. After 3h, the absorbance was read using the Spark Multimode Microplate Reader (Tecan) (492 nm).\u003c/p\u003e\u003cp\u003eApoptosis assay was performed as described in (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Cells were stimulated with 10 ng/ml TGFβ, and 500 \u0026micro;M H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e as a positive control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Evaluation of real-time cell growth using a microfluidic device\u003c/h2\u003e\u003cp\u003eDetailed information on vessel quantification is provided in \u003cb\u003eSupplementary Information\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e2.13 EndMT\u003c/h2\u003e\u003cp\u003eTo evaluate the EndMT, the protocol described in (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) was adapted using 10 ng/ml TGFβ. At 48h after stimulation with TGFβ, images were acquired to evaluate cell morphology, and the cells were lysed with Buffer RL (Norgen Biotek) to proceed with RNA extraction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e2.14. RNA isolation and Real-Time quantitative PCR\u003c/h2\u003e\u003cp\u003eRNA isolation and Real-Time quantitative PCR (RT-qPCR) were performed as described in (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The expression level of the gene of interest was assessed by calculating the Cycle threshold (Ct) value for each gene, which was then normalized against the Ct value of a housekeeping gene (GAPDH or TBP), using the ΔCt or ΔΔCt methods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using GraphPad Prism 10.0. Differences between groups were assessed using either a paired Student\u0026rsquo;s t-test or a non-parametric signed-rank Wilcoxon test, based on data distribution. The normality of the data was evaluated both visually and with the Kolmogorov-Smirnov test. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Characterization of ovarian vasculature and endothelial cells in BRCA1-mut vs WT patients\u003c/h2\u003e\u003cp\u003eOvary specimens were obtained from healthy patients (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9, categorized as WT) undergoing oophorectomy either due to Gender Dysphoria (GD; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) or for other gynaecological conditions (\u003cem\u003ee.g.\u003c/em\u003e, paraovarian fibroma, endometrial hyperplasia; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3). Ovarian biopsies were obtained from women carrying the BRCA1 mutation during preventive oophorectomy (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10, classified as mut) (\u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e). The mean age of the WT group is 36.6 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;13), while for the mut group is 42 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;11).\u003c/p\u003e\u003cp\u003eWe initially investigated differences in vascularization between WT and mut ovarian tissues. IHC analysis involving CD34 (\u003cb\u003eFig.\u0026nbsp;1B,C\u003c/b\u003e) and CD31 (\u003cb\u003eFig.\u0026nbsp;1D,E\u003c/b\u003e) staining, along with quantification using QuPath, revealed no significant differences in the number of CD31\u003csup\u003e+\u003c/sup\u003e mature vessels (\u003cb\u003eFig.\u0026nbsp;1E\u003c/b\u003e), but a higher number of CD34\u003csup\u003e+\u003c/sup\u003e newly formed vessels in the mut tissue (\u003cb\u003eFig.\u0026nbsp;1C\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo investigate whether the differences in vascularization were due to different angiogenic behaviours, we isolated ECs from both types of tissues, successfully obtaining pure EC populations (\u003cb\u003eFig.\u0026nbsp;1F\u003c/b\u003e). The cells were found to be nearly 100% positive for CD31 (vascular endothelium marker), approximately 3\u0026ndash;4% positive for LYVE1 (lymphatic endothelial marker), and 10\u0026ndash;15% positive for CD90 (pericyte marker). Additionally, we confirmed a reduced expression of the BRCA1 gene and protein in mut compared to WT cells (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Further expansion of these cells revealed both morphological (\u003cb\u003eFig.\u0026nbsp;1G\u003c/b\u003e) and growth differences, prompting us to investigate these findings in greater depth.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.2. RNA-Seq for uncovering differential gene expression between WT and BRCA1-mut OVECs\u003c/h2\u003e\u003cp\u003eTo gain a broad view of the potential differences between the two endothelial cell populations, we conducted a transcriptome analysis. RNA sequencing analysis was conducted on 6 WT versus 6 mut OVEC populations to identify DETs between the two groups. PCA clustered the samples into two clearly distinct groups: WT and BRCA1-mut cells (\u003cb\u003eFig.\u0026nbsp;2A\u003c/b\u003e). Differential gene expression analyses identified 11547 differentially transcripts, including 694 up-regulated and 6239 down-regulated transcripts (|log2 fold change|\u0026gt;1, p-value adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As shown in \u003cb\u003eFig.\u0026nbsp;2B\u003c/b\u003e, the differential transcripts are consistent across the samples when comparing the two different biological groups. Given the elevated number of down-regulated transcripts, we decided to focus on the canonical transcripts as annotated in Ensembl database, thus obtaining 76 up-regulated and 1253 down-regulated DETs (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). To perform functional annotation of the gene lists we utilized Qiagen IPA, obtaining disregulated Disease/Functions, the Canonical Pathways, Upstream Regulators, and Regulators Effects (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). Several pathways related to cell proliferation, viability and angiogenesis are predicted as differentially regulated, as well as elements related to cell movements and migration (\u003cb\u003eFig.\u0026nbsp;2C\u003c/b\u003e; \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e), such as RHO GTPase cycle, Stahmin1, PTEN signalling, and RHODI signalling.\u003c/p\u003e\u003cp\u003eFor the regulatory aspect (\u003cb\u003eFig.\u0026nbsp;2D\u003c/b\u003e), several transcription factors related to cell proliferation and migration, like FOXM1 and HGF, and their related target downregulation may have a critical role in the observed phenotypes.\u003c/p\u003e\u003cp\u003eWe next compared our differentially expressed gene list against the repository of published signatures on the Rummagene website to benchmark our results against existing studies. Thus, we identified 478 individual publication-derived gene sets (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 27 of which are annotated to epithelial\u0026ndash;mesenchymal transition. Among these gene sets, EndMT-associated transcriptional signatures, as described in PMC6291168, PMC7080988, and PMC11841332 (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), were systematically interrogated and were found to display consistently higher scores in WT compared to mut OVECs (\u003cb\u003eFig.\u0026nbsp;2E, Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3. BRCA1-mut OVECs express a lower angiogenic receptor signature but exhibit a stronger intrinsic functional angiogenic behaviour\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on RNA-Seq results (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e, we aimed to evaluate angiogenic responses. First, we confirmed the differential expression of angiogenic receptors (\u003cb\u003eFig.\u0026nbsp;3\u003c/b\u003e), with an overall lower expression in mut cells, except for \u003cem\u003eNRP1\u003c/em\u003e and \u003cem\u003ePDGFRA\u003c/em\u003e (\u003cb\u003eFig.\u0026nbsp;3A-G\u003c/b\u003e). This pattern was confirmed by a statistically significant reduction of the \"angiogenic signature\" (\u003cb\u003eFig.\u0026nbsp;3H\u003c/b\u003e). No differences were found in soluble factors (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). Functional assay revealed significant differences in migration (\u003cb\u003eFig.\u0026nbsp;3I\u003c/b\u003e), tube formation (\u003cb\u003eFig.\u0026nbsp;3J\u003c/b\u003e), and viability (\u003cb\u003eFig.\u0026nbsp;3K\u003c/b\u003e) between the two groups, most pronounced under serum deprivation (SFM). In this context, the mut cells consistently displayed enhanced angiogenic activity, highlighting their increased pro-angiogenic potential. Cell proliferation was investigated by cell count after 24h of culture in the presence or absence of serum (\u003cb\u003eFig.\u0026nbsp;3L\u003c/b\u003e). Additionally, we utilized an innovative real-time cell growth prototype (\u0026ldquo;TICheP system\u0026rdquo;), confirming that the mut OVECs reached confluence more rapidly (\u003cb\u003eFig.\u0026nbsp;3M-O\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eSince oxygen deficiency frequently occurs in the tumour microenvironment, we investigated whether mut OVECs respond differently to hypoxia. Thus, we tested cell viability in SFM under normoxia and at an oxygen partial pressure (pO\u003csub\u003e2\u003c/sub\u003e) of 2%. No differences in viability were observed between mut and WT cells, although activation of the hypoxic pathway was confirmed by VEGF upregulation (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Gene network analysis indicative of angiogenic properties dysregulation in BRCA1-mut OVECs\u003c/h2\u003e\u003cp\u003eUpstream regulator analysis performed identified putative upstream molecules, including transcription factors, enzymes, and soluble factors, that are predicted to be activated or inhibited and may account for the transcriptional changes detected in our dataset. The analysis of differentially expressed genes in mut OVECs revealed distinct regulatory networks potentially underlying the altered angiogenic phenotype. Notably, LAMA4 emerged as an activated upstream regulator (Z-score\u0026thinsp;=\u0026thinsp;3.1), whereas both HGF and VEGF were predicted to be inhibited (Z-scores=\u0026ndash;5.3 and \u0026minus;\u0026thinsp;5.5, respectively) (\u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e; \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eA closer inspection of the mechanistic networks highlighted specific regulatory connections for LAMA4, HGF, and VEGFA family members. The LAMA4 network displayed multiple downstream targets involved in extracellular matrix organization and cell adhesion (ITGA8, LAMC1, and LAMC3), cell migration (TMOD1), and angiogenic signalling (FLT1, LIFR, PLXND1, and TGFBR2), consistent with its established role in vascular basement membrane dynamics. Conversely, the HGF and VEGFA family networks appeared broadly inhibited, with reduced activation of canonical angiogenic pathways, including EFNB2, EPHA4, ITGA2, ITGA6, and ITPR3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.5. BRCA1-mut OVECs have altered adhesion molecule expression and form unfunctional monolayers.\u003c/h2\u003e\u003cp\u003eAs RNA-Seq analysis revealed altered signatures in cell-cell adhesion molecules and EndMT (\u003cb\u003eFig.\u0026nbsp;2E, Supplementary Fig. S5)\u003c/b\u003e, we analyzed the presence and expression of adhesion molecules and endothelial markers (CD31, VE-cadherin, vWF, and TJP1, \u003cb\u003eFig.\u0026nbsp;5A\u003c/b\u003e). The cellular architecture differences observed under the inverted microscope were confirmed by IFs, although no evident differences emerged in terms of protein expression (\u003cb\u003eFig.\u0026nbsp;5B\u003c/b\u003e; \u003cb\u003eSupplementary Fig. S6\u003c/b\u003e). We then assessed cell perimeter, Feret diameter, circularity, and roundness through confocal images of TJP1. In the context of ECs, polygonal ECs exhibit circularity and roundness values close to 1, whereas values close to 0 indicate elongated shapes. We demonstrated that mut OVECs displayed significantly higher values of cell perimeter and Feret diameter (\u003cb\u003eFig.\u0026nbsp;5C,D\u003c/b\u003e), and lower circularity and roundness (\u003cb\u003eFig.\u0026nbsp;5E,F\u003c/b\u003e) compared to WT OVECs.\u003c/p\u003e\u003cp\u003eWe further analysed endothelial (\u003cem\u003ePECAM1\u003c/em\u003e, \u003cem\u003eCDH5\u003c/em\u003e, \u003cem\u003eVWF\u003c/em\u003e, and \u003cem\u003eTJP1\u003c/em\u003e) and mesenchymal (\u003cem\u003eFN1\u003c/em\u003e, and \u003cem\u003eCOL1A2\u003c/em\u003e) markers through RT-qPCR. We confirmed RNA-Seq results, observing higher expression of endothelial markers in WT cells (\u003cb\u003eFig.\u0026nbsp;5G-J\u003c/b\u003e), as supported by the EC signature (\u003cb\u003eFig.\u0026nbsp;5K\u003c/b\u003e). In contrast, the expression of mesenchymal markers did not differ significantly, although their levels were generally lower in WT populations (\u003cb\u003eFig.\u0026nbsp;5L,M\u003c/b\u003e), particularly when considering the endothelial to mesenchymal marker ratio (\u003cb\u003eFig.\u0026nbsp;5N\u003c/b\u003e). The reduced gene expression of CD31/PECAM-1 was validated at protein level using flow cytometry (\u003cb\u003eFig.\u0026nbsp;5O-P\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo investigate whether changes in adhesion molecule expression affect permeability, we performed leakage assays using the transwell (TW) model (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). OVECs were grown to confluence in the upper chamber of TWs, and treated with Histamine (HIS) or Bradykinin (BK) to assess vascular permeability. FITC-conjugated BSA was added to the upper chamber, and fluorescence in the lower chamber was evaluated after 5, 15, and 30 min. As shown in \u003cb\u003eFig.\u0026nbsp;5Q\u003c/b\u003e, the permeability of mut OVECs was more variable compared to WT OVECs in resting conditions. Furthermore, the response of mut OVECs to vasoactive stimuli was more intense and constant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.6. BRCA1-mut OVECs are more prone to EndMT transition.\u003c/h2\u003e\u003cp\u003eThe enrichment of EndMT-related signatures observed by Rummagene analysis provides strong external validation and is entirely consistent with the mesenchymal phenotypic changes we observe in our model (\u003cb\u003eFig.\u0026nbsp;2E, Supplementary Fig. S5)\u003c/b\u003e. To deepen our understanding of EndMT, we evaluated the expression of some known EndMT markers using RT-qPCR 48h after TGFβ treatment. If EndMT has occurred, the expression levels of endothelial markers are expected to decrease, while mesenchymal markers increase. As we observed cell suffering in WT OVECs after TGFβ treatment, cell viability and apoptosis were investigated, but no effects were observed (\u003cb\u003eSupplementary Fig. S7\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eConversely, a morphological evaluation revealed distinct differences following TGFβ treatment: WT cells maintained a rounded and regular shape, while mut cells exacerbated their elongated/disorganized appearance (\u003cb\u003eFig.\u0026nbsp;6A\u003c/b\u003e). The treatment with TGFβ induced a stronger mesenchymal transition in mut OVECs, significantly downregulating the endothelial gene signature (\u003cb\u003eFig.\u0026nbsp;6B\u003c/b\u003e). We conducted then confocal IF analysis, which demonstrated considerable alterations in morphological regularity and cytoskeletal organization in resting mut OVECs (\u003cb\u003eFig.\u0026nbsp;6C\u003c/b\u003e), differences exacerbated after TGFβ stimulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.7. BRCA1 silencing induces angiogenetic modulation only in serum-free conditions.\u003c/h2\u003e\u003cp\u003eTo better understand the relationship between phenotype and BRCA1 mutation, BRCA1 expression was silenced in HUVECs. This approach developed a robust cellular model as an alternative to OVECs, due to the challenges of obtaining healthy ovary tissue.\u003c/p\u003e\u003cp\u003eWe silenced a pool from 4 different HUVECs to minimize individual variability. The cells were cultured in a serum-free (REST) or complete medium (FBS), after which experiments were conducted (\u003cb\u003eFig.\u0026nbsp;7A,B and Supplementary Fig. S8\u003c/b\u003e). We achieved approximately 50% silencing of BRCA1 expression, which mimicked the heterozygosity of mut OVECs (\u003cb\u003eFig.\u0026nbsp;7C\u003c/b\u003e). Then, we analysed the expression of endothelial markers (\u003cb\u003eFig.\u0026nbsp;7A\u003c/b\u003e) and angiogenic receptors (\u003cb\u003eFig.\u0026nbsp;7B\u003c/b\u003e) using RT-qPCR. Notably, in the presence of serum, we failed to observe differences (\u003cb\u003eSupplementary Fig. S8\u003c/b\u003e). Similarly to OVECs, some differences in gene expression emerged in the absence of serum. Indeed, we observed the downregulation of several genes (\u003cem\u003ei.e.\u003c/em\u003e, \u003cem\u003eCDH5\u003c/em\u003e, \u003cem\u003eTJP1\u003c/em\u003e, \u003cem\u003eKDR\u003c/em\u003e, and \u003cem\u003eFGFR1\u003c/em\u003e) in BRCA1-silenced cells compared to the control. For the other genes, no significant changes were observed. Conversely, while no statistical differences were found in the leakage assay, great variability in permeability responses was noted in ECs with lower BRCA1 expression, as observed in OVECs. Surprisingly, siBRCA1 HUVECs displayed a lower proliferative/migratory rate in scratch assays with 20% FBS, likely due to the lower expression of angiogenic receptors (\u003cb\u003eFig.\u0026nbsp;7E,F\u003c/b\u003e). On the contrary, the real-time proliferation assay using the \u0026ldquo;TiCheP prototype\u0026rdquo; confirmed that BRCA1-silenced HUVECs reached confluence more rapidly (\u003cb\u003eSupplementary Fig. S9\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e3.8. The dysfunctional behaviour of heterozygous ECs is tissue-specific.\u003c/h2\u003e\u003cp\u003eFinally, to understand if the tissue district was able to influence the cell behaviour, we compared ADMECs of mut \u003cem\u003evs\u003c/em\u003e WT patients. First, we investigated endothelial markers (\u003cb\u003eFig.\u0026nbsp;8A\u003c/b\u003e), which were downregulated in mut OVECs. In mut ADMECs, we did not observe a reduced expression of endothelial marker transcripts (\u003cb\u003eFig.\u0026nbsp;8B\u003c/b\u003e), nor of CD31/PECAM-1 by flow cytometry (\u003cb\u003eFig.\u0026nbsp;8C\u003c/b\u003e). However, our hypothesis that the differing behaviour of the endothelium of the two tissue districts was due to varying sensitivity to oestrogen was challenged by the finding that mut OVECs did not respond to oestrogen stimulation (\u003cb\u003eSupplementary Fig. S10A\u003c/b\u003e), although they express the receptors, unlike ADMECs (\u003cb\u003eSupplementary Fig. S10B\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA considerable body of research has explored the role of BRCA1 depletion in cancer development. While extensive investigations have focused on breast cancer and other conditions in which BRCA1 function is entirely lost, the functional alterations associated with heterozygous BRCA1 status remain unclear. An example is a recent study that analysed whole exome sequencing, RNA-seq, and proteomic data of over 100 human Fallopian tube tissues, finding minimal differences between BRCA1/2 carriers and non-carriers prior to loss of heterozygosity\u0026nbsp;(7).\u003c/p\u003e\n\u003cp\u003eLimited information is available regarding the implications of this condition in ovarian tissue, particularly in ECs.\u0026nbsp;One of the earliest studies connecting BRCA1 and endothelial behaviour highlighted a previously unrecognized role of BRCA1 as a gatekeeper of EC survival using an EC-specific BRCA-KO model (26). BRCA1-mutated individuals have fewer mature oocytes after ovarian stimulation and a reduced follicle reserve. Follicle survival and maturation are tied to ovarian angiogenesis\u0026nbsp;(1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observations regarding the connection between the BRCA1 mutation and angiogenesis have an indirect feature. In breast and pancreatic cancers, BRCA1 mutations can promote angiogenesis by affecting CAFs, leading to tumour-supporting inflammation and blood vessel growth (27). An interaction between BRCA1 and HIF-1α was found in human breast cancer, where hypoxia-stimulated VEGF promoter activity and secretion were reduced in BRCA1-silenced cells (27). Danza and colleagues emphasized the role of miR-578 and miR-573 (upregulated in mut breast cancer tissue) in regulating BRCA 1/2-related angiogenesis by targeting key regulators of focal adhesion, VEGF, and HIF-1 signalling pathways (28). Apart from VEGF, it is postulated that BRCA1 can affect other pro-angiogenic factors, particularly angiopoietin-1, by forming a repressive complex with C-terminal binding protein-interacting protein (CtIP) and zinc finger and BRCA1-interacting protein with KRAB domain-1 (ZBRK1), which then inhibits the expression of angiopoietin-1 (29). Our observations showed that mut OVECs exhibited lower expression levels of angiogenic receptors, while we did not observe differences in growth factors (\u003cem\u003ee.g\u003c/em\u003e., VEGF), except for angiopoietin 1, consistently with\u0026nbsp;Furuta \u003cem\u003eet al.\u003c/em\u003e (29).\u003c/p\u003e\n\u003cp\u003eThe observed decreased expression of angiogenic receptors, which may appear paradoxical in a tumour context, is consistent with the functional behaviour of the cells under SFM conditions. Specifically, mut OVECs exhibited greater angiogenic activity than WT OVECs only under SFM, indicating that the mutation confers an intrinsic capacity for proliferation, migration, and neovascularization that is less reliant on exogenous microenvironmental cues. This interpretation is further supported by the IPA network, which revealed inhibition of both HGF and VEGFA signalling alongside activation of LAMA4. Together, these findings point to a shift from classical growth factor-dependent angiogenesis toward a compensatory mechanism based on extracellular matrix remodelling. In addition, our results indicate that mut OVECs undergo morphological and molecular changes consistent with a partial EndMT. The loss of endothelial integrity, heightened response to vasoactive stimuli, and enrichment of EndMT-related gene signatures suggest that BRCA1 deficiency increases endothelial plasticity and susceptibility to pro-mesenchymal cues. Such vascular alterations may create a permissive microenvironment that facilitates early tumour development and could serve as potential biomarkers or therapeutic targets in BRCA1-associated diseases.\u003c/p\u003e\n\u003cp\u003eBRCA1 inhibits VEGF gene transcription via ER-α (30), and this connection could explain the tissue-specificity observed. The behaviour of BRCA1-silenced HUVECs did not fully correspond to that observed in mut OVECs. This discrepancy is likely attributable to the fact that \"acute\" silencing may not evoke the same biological effects as \"chronic\" gene deficiency. Additionally, the diminished expression of hormone receptors in HUVECs could lead to behaviour akin to that observed by ADMECs.\u003c/p\u003e\n\u003cp\u003eOur study is the first to reveal a significant alteration in the vascular density of newly formed vessels (CD34\u003csup\u003e+\u003c/sup\u003e) within the ovarian tissue of women carrying the BRCA1 mutation. Since 1997, a correlation between colour Doppler ultrasound and microvessel density in detecting angiogenetic differences between benign and malignant ovarian tumours has been demonstrated (31, 32). This finding raises the possibility that \u003cem\u003ein vivo\u003c/em\u003e imaging modalities capable of assessing tissue vascularization, such as Doppler ultrasound, could be harnessed to enhance screening and early detection strategies for individuals with this genetic predisposition.\u003c/p\u003e\n\u003cp\u003eOne limitation of this study is the low number of populations examined since the challenges of obtaining biopsies from healthy ovaries, particularly WT, often precluded the attainment of robust statistical significance; the small size of biopsies hindered the isolation of a sufficient number of cells, requiring the \u003cem\u003ein vitro\u003c/em\u003e expansion of OVECs. Moreover, by deliberate choice, we did not utilize cells beyond the fifth passage to maintain intact endothelial features. Nonetheless, the results of this study are corroborated by multiple lines of evidence gathered through various experimental methods, all leading to the same conclusion.\u003c/p\u003e\n\u003cp\u003eCertainly, the key findings of this study indicate that both in vitro and ex vivo evaluations demonstrate endothelial dysfunction in ovarian tissue associated with the heterozygous mutation, which could be considered a new diagnostic and therapeutic target for further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by the Regional Ethical Committee of FVG (CEUR, Udine, Italy; prot. 0010144/P/GEN/ARCS 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSilvia Pegoraro, Barbara Fogar and Mariagiulia Spazzapan\u003c/strong\u003e: Writing – review \u0026amp; editing; Writing – original draft; Visualization; Validation; Software; Methodology; Investigation; Formal analysis; Data curation; Conceptualization. \u003cstrong\u003eGiulia Canarutto\u003c/strong\u003e: Visualization; Validation; Software; Methodology; Investigation; Formal analysis; Data curation; Writing – original draft. \u003cstrong\u003eAndrea Balduit\u003c/strong\u003e: Methodology; Data curation; Investigation; Writing – review \u0026amp; editing. Formal analysis. \u003cstrong\u003eGabriella Zito\u003c/strong\u003e: Resources; Data curation; Formal analysis.\u0026nbsp;\u003cstrong\u003eAlessandro Mangogna\u003c/strong\u003e: Data curation; Formal analysis; Writing – original draft. \u003cstrong\u003eMiriam Toffoli\u003c/strong\u003e: Investigation; Methodology; Data curation.\u003cstrong\u003e\u0026nbsp;Giovanni Papa\u003c/strong\u003e: Resource; Validation.\u003cstrong\u003e\u0026nbsp;Luca Spazzapan\u003c/strong\u003e: Resource. \u003cstrong\u003eFrancesca Rossi\u003c/strong\u003e: Methodology.\u003cstrong\u003e\u0026nbsp;Eva Andreuzzi\u003c/strong\u003e: Data curation; Writing – review \u0026amp; editing \u003cstrong\u003eFederico Romano\u003c/strong\u003e: Resource. \u003cstrong\u003eSilvano Piazza\u003c/strong\u003e: Visualization; Software; Methodology; Investigation; Formal analysis; Data curation; Writing – original draft. \u003cstrong\u003eChiara Agostinis\u003c/strong\u003e: Conceptualization, Investigation; Data curation; Formal analysis; Writing – review \u0026amp; editing; Writing – original draft; Project administration.\u003cstrong\u003e\u0026nbsp;Giuseppe Ricci\u003c/strong\u003e: Conceptualization; Editing; Formal analysis; Funding acquisition; Writing – review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;Roberta Bulla\u003c/strong\u003e: Conceptualization; Supervision; Funding acquisition; Resources; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the raw RNA-seq data generated from this study have been uploaded to the NCBI Gene Expression Omnibus (GEO), and the accession number is GSE308525.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr Martina Palmieri and Giorgia Meshini for help with patient enrolment, Dr Roberto Carrano for\u0026nbsp;real-time cell growth experiments using a microfluidic device.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ministry of Health, Rome - Italy, in collaboration with the Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste – Italy (RC23/18 and RC20/23 to G.R.) and was funded by European Union - Next Generation EU (D40-RPRIN22BULLA_01).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang X, Niu J, Che T, Zhu Y, Zhang H, Qu J. 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Anticancer Res. 2020;40(12):6923-31.\u003c/li\u003e\n\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":"Angiogenesis, endothelial cells, BRCA1 mutations, EndMT","lastPublishedDoi":"10.21203/rs.3.rs-7980820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7980820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBRCA1/2 mutations are classically associated with hereditary breast and ovarian cancer, yet growing evidence indicates that even heterozygous BRCA1 status may alter ovarian physiology before malignant transformation. Carriers of BRCA1 mutations display reduced ovarian reserve and accelerated reproductive ageing, but the cellular and molecular mechanisms behind these changes remain unclear. Given the fundamental role of angiogenesis in follicle survival and stromal homeostasis, we investigated whether BRCA1 haploinsufficiency disrupts the ovarian microvascular environment, predisposing to both impaired ovarian function and a pro-tumorigenic niche.\u003c/p\u003e\n\u003cp\u003eWe isolated ovarian endothelial cells (OVECs) from biopsies of healthy women, carrying and non-carrying the BRCA1 mutation. Our observations indicated distinct growth behaviours \u003cem\u003ein vitro\u003c/em\u003e, particularly in terms of morphology and replication rate. Transcriptomic analysis revealed a distinct gene expression profile in mut compared to WT OVECs. Mut cells exhibited an enrichment of gene signatures associated with vascular remodelling, such as migration, proliferation, and sensitivity to endothelial-to-mesenchymal transition. Functionally, mut OVECs showed increased angiogenic behaviour and a shift toward mesenchymal traits. Histological analysis of ovarian tissues confirmed aberrant vascular architecture and increased microvessel density in BRCA1-mut ovaries, consistent with endothelial activation and remodelling.\u003c/p\u003e\n\u003cp\u003eIn conclusion, phenotypic and functional differences between wild-type and mut OVECs were proved, demonstrating that heterozygous mutations in BRCA1 can induce a tissue-specific endothelial dysfunction.\u003c/p\u003e","manuscriptTitle":"The impact of heterozygous BRCA1 mutations on ovarian angiogenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 12:03:30","doi":"10.21203/rs.3.rs-7980820/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":"6cbdc905-3843-4ea3-a933-06fc607b79a6","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58953017,"name":"Health sciences/Pathogenesis/Immunopathogenesis"},{"id":58953018,"name":"Biological sciences/Cancer/Tumour angiogenesis"}],"tags":[],"updatedAt":"2026-03-03T14:41:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 12:03:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7980820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7980820","identity":"rs-7980820","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}