Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.03.27.645815

Computational Modeling of the Biphasic Depletion of Ovarian Follicle Reserve and the Chemical Effects on Ovarian Aging

Abstract

IntroductionHuman ovaries begin development in utero. Through oogenesis, the numbers of oocytes and primordial follicles peak to a few million during fetal development, then decline to hundreds of thousands per ovary at birth. These primordial follicles do not regenerate and are thus regarded as the ovarian reserve. Over the life course, the reserve continues to deplete, due to atresia and activation, until menopause when about 1000 primordial follicles remain. Exposure to chemotherapy drugs and environmental pollutants can accelerate follicular depletion potentially leading to a greater risk of early menopause, primary ovarian insufficiency (POI), and infertility. Physiologically, the ovarian reserve is depleted in a seemingly biphasic pattern - a slow steady decline from birth to mid-30s, followed by a faster decline to menopause which typically occurs around age 50 years. While this depletion pattern has been described with empirical mathematical formulations, rarely is it modeled mechanistically. A mechanic model that can characterize the dynamics of follicular depletion throughout the life course will help researchers better understand and predict the impact of chemical exposures on ovarian aging. MethodsHere we propose a minimal mechanistic model, which includes (1) a zero-order feedforward inhibition of primordial follicle activation by a local autocrine/paracrine inhibitory factor secreted by the primordial follicles, and (2) a high-gain feedback inhibition of primordial follicle activation by the anti-Mullerian hormone (AMH) secreted by the growing (primary, secondary, and early antral) follicles. The model is configured such that the two regulatory processes prevent primordial follicles from premature overactivation in early and late reproductive life stages, respectively. Two exposure scenarios - chemo-drugs/radiation and tobacco smoke - are presented to demonstrate predictive robustness and biological plausibility of chemically induced increases in cellular atresia. ResultsOur model recapitulates the biphasic depletion curve and predicts a constant supply of growing follicles through most of the active reproductive lifespan. This model predicts that the size of the initial primordial follicle pool plays the most significant role in determining menopausal age and suggests that unilateral ovariectomy may have a more attenuated effect than expected. Simulations of transient exposure to chemotherapy drugs provide an exposure example for promoting atresia of primordial and/or growing follicles and suggest exposure at earlier ages have greater impact on ovarian reserve and menopausal timing than exposure at later ages. Also, simulations of chronic chemical exposures suggest that chemicals which directly promote primordial follicle atresia are more damaging than chemicals directly promoting growing follicle atresia or inhibiting AMH, potentially leading to earlier age at menopause. A specific scenario of chronic exposure to cigarette smoke of various intensities was simulated to validate the prediction power of the model. ConclusionsThe ovary may have compensatory factors to extend reproductive age as long as possible amid insults that reduce the primordial follicle pool. The timing of these insults are likely an important variable. Future elaborations of such mechanistically based computational modeling with integration of in vitro toxicity testing data may help scaling efforts in predicting the implications of reproductive toxicants on ovarian aging.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Moyd, S. A., Xiao, S., Gaskins, A. J., Zhang, Q.. 2025-04-01. Computational Modeling of the Biphasic Depletion of Ovarian Follicle Reserve and the Chemical Effects on Ovarian Aging. https://doi.org/10.1101/2025.03.27.645815

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

SpaReg: sparsity-based 3D reconstruction of tissue microenvironments at native resolution across morphological and spatial molecular modalities

Tissue microenvironments comprise cellular and acellular components whose three-dimensional (3D) architecture guides disease fate. Direct imaging of intact specimens by light-sheet and multiphoton microscopy, and computational reconstruction from serial sections, have established that 3D spatial context reveals cell and tissue organization inaccessible at single planes. Computational reconstruction in particular can leverage archived human tissue, benefiting from the cost-effectiveness, robustness, scalable storage, workflow compatibility, and century-long pathobiology knowledge of histology, and can integrate multiple spatial modalities. However, sectioning can introduce tears and folds, and computational alignment can further distort tissue integrity. Here we introduce SpaReg, a sparsity-based 3D reconstruction method spanning histology, spatial proteomics and spatial transcriptomics. Across multiple organs, SpaReg robustly reconstructs large tissue volumes with preserved subcellular morphology despite sectioning artifacts. On a standardized histology benchmark, SpaReg achieves the best balance between 3D reconstruction accuracy and tissue integrity, and on spatial transcriptomics benchmarks it ranks among the leading methods while scaling to hundreds of sections and millions of cells in a dataset that several existing methods fail to process. Preservation of subcellular morphology by SpaReg also enables training of a Hematoxylin and Eosin (H&E)-based epithelial, T and B cell classifier, generating single-cell-resolved 3D maps directly from H&E. Applied to pancreatic tissue containing pancreatic ductal adenocarcinoma arising from an intraductal papillary mucinous neoplasm, these maps reveal that 2D sections overestimate immune exclusion, and resolve lymphoid aggregates in 3D. SpaReg, therefore, provides a scalable foundation for morphologically faithful, multimodal 3D atlases and spatially informed disease modeling

systems biology↗

TxCyto: A machine learning framework for estimating cytokine activity from whole transcriptome

Cytokines are critical mediators of intercellular communication, and a comprehensive characterization of their activity is essential for understanding health and disease. Existing tools to infer cytokine activity rely on experimental measurements. However, such measurements are available only for a small minority (43) of cytokines, and moreover, cytokine activity and response are highly context-specific, making a comprehensive experimental profiling across tissues, disease states, and biological contexts impractical. To address this gap, we developed TxCyto - a deep learning-based framework that infers the activity of cytokines, and more broadly of the tumor secretome, directly from the whole transcriptome profile of a sample. Trained on pan-cancer TCGA tumor transcriptomes, TxCyto was extensively validated in multiple independent datasets, including cytokine perturbation experiments. Across multiple cancer immunotherapy cohorts, TxCyto identified cytokines whose predicted activity was associated with therapeutic response. Furthermore, in spatial transcriptomic data for Liver cancer, TxCyto discovered spatial niches associated with response to immunotherapy. Overall, we develop a machine learning tool -TxCyto, for predicting the activity of 645 cytokines and tumor secretome from readily available whole transcriptomes. The TxCyto framework is generally applicable to other classes of regulatory molecules and TxCyto code base, and the tools are provided at https://github.com/Rahulncbs/TxCyto.

systems biology↗

Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions

Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.

systems biology↗