Systems biology of the Unfolded Protein Response
Humans evolved the systems to manage energy in conditions where resources were largely scarce and demands on energy were very high. However, many humans now live in society where there is an excess of nutrients especially sugars and fats.
Thus the adaptive mechanisms that manage energy and nutrient use have not evolved to cope with such environmental conditions, and coupled with increasingly sedentary lifestyles and increasing lifespan, the failure to adapt to dietary excess leads to metabolic syndromes such as type 2 diabetes.
The Endoplasmic Reticulum (ER) is the principal organelle dedicated to the folding and transport of a diverse range of newly synthesized proteins. Networks of regulatory enzymes monitor cellular conditions such as the levels of available nutrients, and in turn regulate the capacity of the ER. ER capacity must be tightly controlled in order to ensure that protein production can meet demand, or that energy is not wasted in periods of stress.
One critical system that is engaged to increase ER capacity when the ER is overwhelmed is called the Unfolded Protein Response (UPR). The UPR is an allostatic response that acts in several different ways to enhance the protein-folding function of the ER, decrease the level of peptides inputted into the ER, and promote cellular survival.
However, long-term engagement of the UPR likely plays a role in promoting the onset of chronic inflammatory states that can lead to disease. Yet we know very little of the genes involved in turning the UPR on or off. Identifying these genes in the first step in the design of therapies that can be used to modulate UPR activity during the treatment of diseases such as diabetes that are driven by inflammation.
In higher eukaryotes, the UPR consists of at least three different branches triggered by the stress-sensing proteins ATF6∝, PERK, and the highly conserved IRE1∝. Components of the three branches of the UPR are known to engage in cross talk with multiple signaling networks in both physiological and pathological circumstances, such as those which mediate insulin action and regulate autophagy. Signaling cross-talk likely plays a large role in determining the consequences of UPR activation (e.g. survival versus apoptosis).
As activation and/or deregulation of the UPR has been reported to play a relevant role on complex pathological entities such as type 2 diabetes, neurodegeneration, and cancer, understanding UPR on a systems-level is important for both fully understanding these pathologies and achieving successful therapeutic design. For instance, specific types of cancer such as myeloma may be particularly sensitive to intervention of the UPR.
We are gaining systems-level insights in the role of ER stress in disease by performing RNAi screens for genes involved in all three branches of the UPR. We then aim to generate probabilistic models of the dynamic and hierarchical relationships between these genes through computational integration of functional genomic data with orthogonal datasets.
Control of Reactive Oxygen Species
Reactive Oxygen Species (ROS) such as superoxide (O2-), free radicals, and peroxides are natural by-products of oxygen metabolism that also activate signaling networks regulating a spectrum of cellular behaviors including proliferation, differentiation, death, and morphogenesis. While the notion that ROS are causal to ageing has been questioned as of late, high levels of ROS have been clearly been linked to diseases such as diabetes, cancer, as well as neurodegenerative and cardiovascular disorders.
Eukaryotic cells have evolved a complex regulatory system in order to prevent excessive accumulation of ROS during normal metabolic processes, while maintaining the ability to raise ROS levels as needed.
However little is known about the architecture and dynamics of this system. Because the manipulation of ROS levels represents a powerful strategy to prevent a variety of diverse diseases, gaining a comprehensive and quantitative description of the ROS regulatory system is warranted.
In order to understand how ROS levels are genetically controlled, we have performed a genome-wide RNAi screen for regulators of ROS in Drosophila cells and have identified a number of both known and novel genes that are enhancers or suppressors of superoxide levels.
Inhibition of Drosophila dorsal/dl, which encodes an Nf-kB/Rel protein, was isolated as a suppressor of ROS consistent with previous studies in mammalian systems that have demonstrated an antioxidant function for Nf-kB. Interestingly we also isolated cactus/cact, the Drosophila ortholog of the Nf-kB inhibitor IkB as a ROS suppressor. While inhibition and hyperactivation of Nf-kB signaling lead to similar increases in ROS levels, the underlying biochemical mechanisms for the observed increase are likely very different. To begin to understand these differences, we performed two sets of experiments.
First, we profiled genome-wide mRNA expression in Nf-kB and IkB deficient cells in order to determine the transcriptional targets of Nf-kB that may be involved in regulating ROS levels. Second, we performed a series of screens in Drosophila cells for regulators of ROS where we inhibited kinases, phosphatases, and transcription factors (TFs) in sensitized backgrounds in which either Nf-kB or IkB was also inhibited by RNAi.
That many genes isolated in these sensitized screens are also well-characterized regulators of insulin signaling, metabolism, ER stress, and autophagy strongly suggests these cellular processes are normally involved in Nf-kB-mediated ROS production. But these systems-level studies do not provide insight into the biochemical mechanisms by which Nf-kB, Nf-kB genetic interactors, and Nf-kB transcriptional targets are involved in control of ROS.
We are now: (a) performing studies with the aim of understanding how Nf-kB transcriptional dynamics are regulated by the genetic interactors identified by sensitized RNAi screens for regulators of ROS; (b) determining how Nf-kB and its interactors are involved in the control of ROS levels by acting as regulators of ER stress, insulin signaling and autophagy; (c) assembling a comprehensive genome-wide list of ROS regulators that interact with Nf-kB and IkB; and (d) performing computational integration to develop hierarchical and dynamic systems-level models of the Nf-kB signaling networks which regulate ROS levels.