"Getting mixed messages: How p53 controls its dynamics to interpret variable upstream signals"
p53 is one of the most widely studied proteins in molecular biology for its central role in tumorigenesis. In a healthy, replicating cell, p53 makes cell fate decisions based on signals it receives from repair pathways. Not only must p53 consolidate information from multiple sources, the signals it receives do not correspond exactly to the total amount of damage in the cell; rather, comparably lethal amounts of damage can induce dissimilar signals. For example, gamma radiation induces DNA lesions that p53-activating kinases bind to within minutes, while the DNA lesions created by UV radiation are harder for the cell to detect and only communicate with p53-activating kinases during repair.
Using a mechanistic model, we argue that this difference in response speed causes distinct dynamical profiles of p53 to arise. If p53 receives a strong signal with a short duration, as it would for a low dose of gamma radiation, the cell would be susceptible to premature apoptosis if p53 became overactive due to this signal. Instead, causing p53 to oscillate weakens its response and the signal only recovers if the damage persists after the initial round of suppression. For UV radiation, the delay between damage induction and communication to p53 creates a signal that starts low and increases over several hours. An under-regulated system may ignore a weak but long-lasting signal even if it represents extensive DNA damage. Instead, because this system can escape to a bistable region with higher levels of p53 at intermediate levels of activating signal, the cell can compensate for low kinase activation by raising the amount of available substrate. This allows active p53 to accumulate when exposed to a low but durable signal. Other models have focused on the mechanistic cause of p53 oscillations; this model provides a hypothesis as to why they exist.
Here, we focus on the surprising hypotheses that arise from reconciling p53's paradoxical behavior and discuss how this model extends our knowledge of tumor survival strategies.