Systems Biology Approach to Understanding the Cellular Stress Response and Disease Mechanisms
Cellular stress, which can be induced by multiple factors (e.g. excess reactive oxygen species, ionizing radiation, cellular dehydration, excess shearing forces etc.), can result in damage to DNA, proteins, or lipids leading to deleterious cell phenotypes. Cells are programmed to respond to stress by 1) sensing and rectifying factors that lead to cell damage, 2) repairing damage to macromolecules, and 3) appropriately regulating the cell cycle and DNA replication processes, when necessary, to accomplish (1) and (2). While acute stresses lead to the direct activation and expression of acute phase proteins, sustained stresses cause persistent dysfunctions, which then lead to the activation of complex transcriptional and epigenetic programs. The induction of the cellular stress response can result in one of three phenotypic outcomes: 1) cell death, 2) return to homeostasis, or 3) the establishment of an altered state (such as senescence or cancer). Cell fate decisions are largely dependent the degree and duration of stress, the cell type, and ultimately the degree of damage to DNA and other macromolecules.
The primary objective of this research is to characterize response to two different types of cellular stress (oxidative stress and high-fat diet induced toxicity) using a systems biology approach and, in doing so, to identify biomarkers and response mechanisms for each type of stress. The first model of stress (i.e. oxidative stress in endothelial cells) is a study of stress response in vitro. The use of dose and time-dependent measurements reveal key inflection points and mechanisms of stress response. The mechanisms thus elucidated are validated through pharmacological and genetic perturbations followed by phenotypic assays that define the cell fates. The second model of stress response (i.e. a mouse model of high-fat diet induced liver toxicity) exemplifies many of the paracrine regulators of stress response. The comparison to human data reveals the potential for using mouse models in understanding human diseases. In both studies, the combined use of transcriptomic data with existing databases of protein interaction networks and transcription-factor target information provides a global perspective on stress response, which is critically important to consider when designing effective therapeutics with limited off-target effects.