Dose Response Relationship in Anti-Stress Gene Regulatory Networks

Abstract

To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O 2 , is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steadystate dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on the level of local gains, presence of gain-changing events, and degree of feedforward gene activation, this region can appear as superlinear, sublinear, or even J-shaped. The general dose response transition proposed here was further examined in a complex anti-electrophilic stress pathway, which involves multiple genes, enzymes, and metabolic reactions. This work would help biologists and especially toxicologists to better assess and predict the cellular impact brought about by biological stressors.

Unraveling cellular pathways contributing to drug-induced liver injury by dynamical modeling

“…Stress pathways try to repair a 'controlled variable' (e.g. temperature, levels of reactive oxygen species [ROS], or a stressor itself) that is out of control [20]. To this purpose, a stress pathway typically consists of two major components ( Figure 1, top right).…”

“…The first model describing the oxidative stress pathway was constructed by Zhang et al [20], using a homeostatic control system as a basis. This model describes in detail how the Nrf2 pathway responds to stress and how it alters gene expression levels, and ultimately deals with ROS via GSH activity.…”

“…The general aim was to describe antistress gene regulatory networks, to explore the 'design principles' that have evolved, and use the oxidative stress pathway as an example. One of the design principles that was investigated involves the response coefficient [20,28,29]. A response coefficient (also called gain) defines the sensitivity of a system variable of interest to a perturbation when the system is at equilibrium.…”

“…For example, Nrf2 enhances its own production and therefore regulates itself. Stress pathways often combine multiple ways to enhance the response coefficient, which strongly affects the shape of the dose response curve [20].…”

“…Zhang et al's [20,28,29] findings highlight why it is in general important to apply computational modeling to stress pathways on top of experimental work. With respect to the application to oxidative stress in particular, their models were subsequently adjusted to be used for renal cells, in combination with transcriptomic, proteomic, and metabolomic data [30].…”