Recapitulating human ovarian aging using random walks

Mechanism(s) that control whether individual human primordial ovarian follicles (PFs) remain dormant, or begin to grow, are all but unknown. One of our groups has recently shown that activation of the Integrated Stress Response (ISR) pathway can slow follicular granulosa cell proliferation by activating cell cycle checkpoints. Those data suggest that the ISR is active and fluctuates according to local conditions in dormant PFs. Because cell cycle entry of (pre)granulosa cells is required for PF growth activation (PFGA), we propose that rare ISR checkpoint resolution allows individual PFs to begin to grow. Fluctuating ISR activity within individual PFs can be described by a random process. In this article, we model ISR activity of individual PFs by one-dimensional random walks (RWs) and monitor the rate at which simulated checkpoint resolution and thus PFGA threshold crossing occurs. We show that the simultaneous recapitulation of (i) the loss of PFs over time within simulated subjects, and (ii) the timing of PF depletion in populations of simulated subjects equivalent to the distribution of the human age of natural menopause can be produced using this approach. In the RW model, the probability that individual PFs grow is influenced by regionally fluctuating conditions, that over time manifests in the known pattern of PFGA. Considered at the level of the ovary, randomness appears to be a key, purposeful feature of human ovarian aging.

Automated summary

The mechanism(s) controlling whether individual human primordial ovarian follicles remain dormant or begin to grow are largely unknown. However, recent research suggests that activating the Integrated Stress Response (ISR) pathway can slow the proliferation of follicular granulosa cells and delay follicle growth. It is proposed that the resolution of rare ISR checkpoints allows individual follicles to initiate growth. A one-dimensional random walk model is used to simulate ISR activity in individual follicles and monitor the rate at which checkpoint resolution and follicle growth activation occur. The model successfully reproduces the loss of follicles over time and the timing of follicle depletion seen in natural menopause.