Single-cell study of human ovarian response to phthalate exposure reveals glial cell susceptibility and disruption of adhesion and mitochondrial pathways.
The sensitivity of ovarian nervous system cells to MEHP offers new mechanistic insights into reported links between phthalates, altered ovarian sensitivity index, and polyendocrine metabolic ovarian syndrome, and the need for a holistic assessment of ovarian toxicity that considers all ovarian cell types, not only follicles is highlighted.
Abstract
Background
Phthalates are known male endocrine disruptors and reproductive toxicants. Despite growing evidence of female effects, mechanistic knowledge in key reproductive organs remains limited, hindering regulations. Here, we conducted an experimental study to map the impact of mono(2-ethylhexyl) phthalate (MEHP), the primary metabolite of di(2-ethylhexyl) phthalate (DEHP), on adult human ovarian tissue at single-cell resolution.
Methods
Ovarian tissue explants from seven donors (five gender-affirming surgery, two caesarean-section) were exposed to two MEHP concentrations: an epidemiologically relevant 20.51 nM and a 1000-fold higher 20.51 μM. After six days, single-cell RNA sequencing (one donor) and immunostainings (six donors) were used to characterise gene expression changes across cell types. Primary ovarian cells derived from three gender-affirming surgery patients and stem cell-derived Schwann cells were used for validation.
Findings
Explants retained all major ovarian cell types, including rare glial cells and oocytes. MEHP altered transcriptomes across all cell types, disrupting pathways related to actin cytoskeleton, cell adhesion, and oxidative phosphorylation (OXPHOS). Protein analyses confirmed altered EIF5A, MT-ND3, MT-ND4L, and VCL expression, genes linked to mitochondrial translation, OXPHOS, adhesion, and cytoskeleton. MEHP also reduced cell-cell communication, particularly glial-stromal interactions. Mitochondrial stress assay in primary cells indicated increased proton leakage, and Schwann cell responses mirrored the scRNA-seq findings.
Interpretation
Ovarian toxicity studies traditionally focus on follicles, but this study reveals the susceptibility of non-follicular cells. Importantly, the sensitivity of ovarian nervous system cells to MEHP offers new mechanistic insights into reported links between phthalates, altered ovarian sensitivity index, and polyendocrine metabolic ovarian syndrome. Our findings highlight the need for a holistic assessment of ovarian toxicity that considers all ovarian cell types, not only follicles.
Funding
Swedish Research Council for Sustainable Development and the European Union.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.