Obesity arises from excess fat accumulation driven by cellular differentiation into adipocytes, a process known as adipogenesis. Recent experimental studies have implicated excessive stress and disruptions of the circadian rhythm in promoting adipogenic commitment. The preprint summarized here develops a dynamical systems perspective to explain how stress signaling interacting with circadian phase governs the timing and extent of adipogenic differentiation.
Experimental observations indicate two related phenomena: first, chronic or severe stress can enhance the fraction of precursor cells that commit to the adipocyte lineage; second, this increased adipogenic commitment occurs preferentially at particular times of day, implying a dependence on the organism's circadian state. These findings raise a mechanistic question about how time-varying stress signals and circadian oscillations interact with the intracellular regulatory network that controls differentiation.
To address the dynamical question, the authors constructed a mathematical model that focuses on the transcription factor PPARγ, widely recognized as a central regulator of adipogenic differentiation. The model encodes regulatory interactions that capture positive feedback on PPARγ activity with dynamics that are slower than some upstream signals. Stress input signals are represented as external drivers that modulate the regulatory network; the model explicitly considers different temporal patterns of stress, including constant and pulsatile inputs.
A central result of the model is that adipogenic commitment emerges as a bistable and effectively irreversible transition in PPARγ expression/activity. In this framework, precursor cells occupy either a low-PPARγ (undifferentiated) state or a high-PPARγ (committed) state, and the switch to the committed state is governed by a threshold set primarily by a slow positive-feedback regulation of PPARγ. Because the feedback operates on a slow timescale, transient perturbations that do not push PPARγ past the threshold will not produce commitment, whereas sustained or sufficiently large perturbations will drive the irreversible transition.
The model reproduces key qualitative experimental observations concerning how different stress regimes influence adipogenesis. Specifically, simulations capture the differing outcomes evoked by constant stress input versus pulsatile stress patterns: the extent of adipogenic differentiation depends not only on the magnitude of stress but also on its temporal structure. The ability to reproduce both constant and pulsatile input responses supports the model's central mechanism in which the timing and duration of stress relative to the slow PPARγ feedback determine whether the commitment threshold is crossed.
Because adipogenic commitment in the model requires surpassing a PPARγ-dependent threshold that accrues over time, the circadian phase at which stress is applied affects the probability and timing of commitment. The model explains the reported correlation between the time of day when differentiation occurs and the circadian phase: when stress coincides with phases that effectively lower the threshold or align with permissive regulatory states, commitment is more likely and occurs earlier. Conversely, stress signals delivered in less permissive circadian phases are less likely to trigger the irreversible switch.
Beyond reproducing observed phenomena, the model makes testable predictions about interventions that could modulate adipogenic commitment under stressful conditions. The analysis suggests that altering the slow positive-feedback dynamics of PPARγ, changing the timing of stress relative to the circadian cycle, or adjusting the pattern (constant vs pulsatile) and duration of stress inputs could shift the commitment threshold and thereby reduce or increase the probability of differentiation. These predictions identify dynamical leverage points for experimental manipulation and potential therapeutic strategies to limit stress-induced adipogenesis.
This work is presented as a preprint and has not undergone peer review. The summary does not report specific numerical parameter values, detailed model equations, or the datasets used for parameterization and validation; those details may be available in the full manuscript or supplementary materials but were not included in the accessible summary. The model is centered on PPARγ and the described slow positive feedback; mechanistic contributions from other molecular players or systemic factors were not detailed in the summary.
The authors offer a parsimonious dynamical explanation for how stress signaling coupled to circadian phase can gate adipogenic commitment. By centering the model on PPARγ and its slow positive feedback, they show that adipogenic differentiation can be understood as a bistable, effectively irreversible switch whose threshold depends on feedback dynamics and timing of external signals. The model reproduces experimental patterns for constant and pulsatile stress and yields testable predictions about how to control adipogenic commitment during stress-induced circadian disruption. Because this is a preprint, experimental validation and peer review will be important next steps to confirm and extend these findings.